diff --git a/resources_servers/grl_sokoban/README.md b/resources_servers/grl_sokoban/README.md
new file mode 100644
index 000000000..dfe46eb5e
--- /dev/null
+++ b/resources_servers/grl_sokoban/README.md
@@ -0,0 +1,95 @@
+# GRL Sokoban Resource Server
+
+Single-box Sokoban puzzle environment. The environment is implemented under `resources_servers/grl_sokoban/env`, mirroring the sokoban implementation in GRL repo (https://github.com/lmgame-org/GRL) and based on code from https://github.com/lmgame-org/lmenv, developed in collaboration with NVIDIA. The implementation uses gym-sokoban package (https://github.com/mpSchrader/gym-sokoban) which implements DeepMind's paper Imagination Augmented Agents for Deep Reinforcement Learning following the standard of https://gymnasium.farama.org.
+
+## Why it exists
+- **Domain**: Deterministic Sokoban puzzles.
+- **Evaluation**: Agents must push a box onto its target with minimal invalid moves.
+- **Verifier**: `/verify` rewards the cumulative Sokoban score only when `success` is reported by the environment.
+
+## Setup
+
+Please follow the setup instructions as outlined in: https://github.com/NVIDIA-NeMo/Gym/blob/main/docs/tutorials/02-setup.md#step-1-clone-and-install.
+
+## Running
+Spin up the server alongside a compatible agent:
+```bash
+config_paths="responses_api_models/openai_model/configs/openai_model.yaml,\
+resources_servers/grl_sokoban/configs/grl_sokoban.yaml"
+ng_run "+config_paths=[$config_paths]"
+```
+
+Collect trajectories:
+```bash
+ng_collect_rollouts +agent_name=grl_sokoban_simple_agent \
+    +input_jsonl_fpath=resources_servers/grl_sokoban/data/example.jsonl \
+    +output_jsonl_fpath=resources_servers/grl_sokoban/data/example_rollouts.jsonl \
+    +limit=5
+```
+
+Launch the rollout viewer:
+```bash
+ng_viewer +jsonl_fpath=resources_servers/grl_sokoban/data/example_rollouts.jsonl
+```
+
+## Tests
+```bash
+pytest resources_servers/grl_sokoban/tests
+```
+
+## Licensing
+- Code: Apache 2.0
+- Data: Apache 2.0
+
+---
+
+## Reward Profiling Results
+
+### Qwen3-4B
+
+**Dataset**: 3,200 rollouts (200 prompts × 16 repeats)
+
+**Performance Metrics**:
+- **Success Rate**: 13.47% (431/3,200 rollouts)
+- **Mean Reward**: 0.93 (range: -8.90 to 10.90)
+- **Median Reward**: 0.00
+
+**Key Findings**:
+- Most rollouts (66.7%) received reward of 0.00 (no valid actions taken)
+- Successful puzzle solutions achieved rewards of ~10.5-10.9
+- Average 2.64 tool calls per rollout
+- Moderate negative correlation between tool calls and reward (-0.23)
+
+**Top Reward Distribution**:
+- `0.0`: 2,134 rollouts (66.7%) - no valid actions or early termination
+- `10.8`: 206 rollouts (6.4%) - successful puzzle completion
+- `10.9`: 72 rollouts (2.2%) - successful puzzle completion
+- `10.7`: 51 rollouts (1.6%) - successful puzzle completion
+- Negative rewards: Invalid moves or non-optimal solutions
+
+The moderate success rate (13.47%) indicates that Sokoban puzzle-solving requires spatial planning and understanding of box-pushing mechanics. Most failures result from the model not taking valid actions (reward 0.0), while successful completions achieve consistent high rewards (~10.5-10.9). The negative correlation between tool calls and reward suggests that longer sequences often lead to invalid moves or dead-end states.
+
+### Qwen3-30B-A3B
+
+**Dataset**: 3,200 rollouts (200 prompts × 16 repeats)
+
+**Performance Metrics**:
+- **Success Rate**: 38.56% (1,234/3,200 rollouts)
+- **Mean Reward**: 4.00 (range: -5.40 to 10.90)
+- **Median Reward**: 0.00
+
+**Key Findings**:
+- Most rollouts (43.9%) received reward of 0.00 (no valid actions taken)
+- Successful puzzle solutions achieved rewards of ~10.5-10.9
+- Average 2.10 tool calls per rollout
+- Moderate positive correlation between tool calls and reward (0.22)
+
+**Top Reward Distribution**:
+- `0.0`: 1,405 rollouts (43.9%) - no valid actions or early termination
+- `10.8`: 477 rollouts (14.9%) - successful puzzle completion
+- `10.6`: 183 rollouts (5.7%) - successful puzzle completion
+- `10.7`: 172 rollouts (5.4%) - successful puzzle completion
+- `10.9`: 157 rollouts (4.9%) - successful puzzle completion
+- Negative rewards: Invalid moves or non-optimal solutions
+
+The higher success rate (38.56%) compared to Qwen3-4B indicates that the larger model performs significantly better at spatial planning and understanding box-pushing mechanics. While the majority of failures still result from not taking valid actions (reward 0.0), the model achieves nearly 3x the success rate of the 4B variant. The positive correlation between tool calls and reward suggests that the model can effectively use longer action sequences to solve puzzles.
diff --git a/resources_servers/grl_sokoban/app.py b/resources_servers/grl_sokoban/app.py
new file mode 100644
index 000000000..49ee04456
--- /dev/null
+++ b/resources_servers/grl_sokoban/app.py
@@ -0,0 +1,237 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from typing import Any, Dict, List, Optional, Union
+
+from fastapi import Body, FastAPI, HTTPException, Request
+from pydantic import BaseModel, Field
+
+from nemo_gym.base_resources_server import (
+    BaseResourcesServerConfig,
+    BaseSeedSessionRequest,
+    BaseSeedSessionResponse,
+    BaseVerifyRequest,
+    BaseVerifyResponse,
+    SimpleResourcesServer,
+)
+from nemo_gym.server_utils import SESSION_ID_KEY
+from resources_servers.grl_sokoban.sokoban_env import SokobanEnv
+
+
+DEFAULT_GRID_LOOKUP = {0: "#", 1: "_", 2: "O", 3: "√", 4: "X", 5: "P", 6: "S"}
+DEFAULT_ACTION_LOOKUP = {1: "Up", 2: "Down", 3: "Left", 4: "Right"}
+
+
+class GrlSokobanResourcesServerConfig(BaseResourcesServerConfig):
+    env_config: Dict[str, Any] = Field(
+        default_factory=lambda: {
+            "grid_lookup": DEFAULT_GRID_LOOKUP,
+            "action_lookup": DEFAULT_ACTION_LOOKUP,
+            "search_depth": 100,
+            "dim_room": (6, 6),
+            "max_steps": 100,
+            "num_boxes": 1,
+            "render_mode": "text",
+        }
+    )
+
+
+class GrlSokobanSeedSessionRequest(BaseSeedSessionRequest):
+    seed: Optional[int] = None
+
+
+class GrlSokobanSeedSessionResponse(BaseSeedSessionResponse):
+    observation: str
+
+
+class GrlSokobanStepRequest(BaseModel):
+    actions: List[Union[str, int]] = Field(default_factory=list)
+
+
+class GrlSokobanStepTrace(BaseModel):
+    action_id: int
+    action_label: str
+    reward: float
+    done: bool
+    info: Dict[str, Any]
+
+
+class GrlSokobanStepResponse(BaseModel):
+    observation: str
+    reward: float
+    total_reward: float
+    done: bool
+    steps: List[GrlSokobanStepTrace]
+    history: List[GrlSokobanStepTrace] = Field(default_factory=list)
+
+
+class GrlSokobanVerifyResponse(BaseVerifyResponse):
+    success: bool
+
+
+@dataclass
+class SokobanSessionState:
+    env: Any
+    observation: str
+    total_reward: float = 0.0
+    done: bool = False
+    last_info: Dict[str, Any] = field(default_factory=dict)
+    history: List[GrlSokobanStepTrace] = field(default_factory=list)
+
+
+class GrlSokobanResourcesServer(SimpleResourcesServer):
+    config: GrlSokobanResourcesServerConfig
+    session_id_to_state: Dict[str, SokobanSessionState] = Field(default_factory=dict)
+
+    def setup_webserver(self) -> FastAPI:
+        app = super().setup_webserver()
+        app.post("/step")(self.step)
+        return app
+
+    def _create_env(self):
+        return SokobanEnv(self.config.env_config)
+
+    async def seed_session(
+        self, request: Request, body: GrlSokobanSeedSessionRequest
+    ) -> GrlSokobanSeedSessionResponse:
+        session_id = request.session[SESSION_ID_KEY]
+        env = self._create_env()
+        observation = env.reset(seed=body.seed)
+
+        self.session_id_to_state[session_id] = SokobanSessionState(
+            env=env,
+            observation=observation,
+        )
+
+        return GrlSokobanSeedSessionResponse(observation=observation)
+
+    async def step(self, request: Request, body: Any = Body()) -> GrlSokobanStepResponse:
+        session_id = request.session.get(SESSION_ID_KEY)
+        if session_id is None or session_id not in self.session_id_to_state:
+            raise HTTPException(status_code=400, detail="Session not initialized. Call /seed_session first.")
+
+        # Handle both formats: {"actions": [...]} and [...]
+        # This makes the endpoint more robust to handle cases where the model
+        # might send just the array instead of the expected object format
+        if isinstance(body, list):
+            # If body is directly a list, wrap it in the expected format
+            parsed_body = GrlSokobanStepRequest(actions=body)
+        elif isinstance(body, dict):
+            # If body is a dict, try to parse it normally
+            try:
+                parsed_body = GrlSokobanStepRequest.model_validate(body)
+            except Exception as e:
+                raise HTTPException(
+                    status_code=422,
+                    detail=f"Invalid request format. Expected {{'actions': [...]}} or [...], got: {body}. Error: {str(e)}",
+                )
+        else:
+            raise HTTPException(
+                status_code=422,
+                detail=f"Invalid request format. Expected {{'actions': [...]}} or [...], got: {type(body).__name__}",
+            )
+
+        session_state = self.session_id_to_state[session_id]
+        env = session_state.env
+
+        reverse_lookup = {label.lower(): idx for idx, label in env.ACTION_LOOKUP.items()}
+        total_step_reward = 0.0
+        steps: List[GrlSokobanStepTrace] = []
+
+        if session_state.done:
+            return GrlSokobanStepResponse(
+                observation=session_state.observation,
+                reward=0.0,
+                total_reward=session_state.total_reward,
+                done=True,
+                steps=[],
+                history=list(session_state.history),
+            )
+
+        for action in parsed_body.actions:
+            action_id = self._parse_action(action, reverse_lookup)
+            if action_id not in env.ACTION_LOOKUP:
+                raise HTTPException(status_code=400, detail=f"Invalid action identifier: {action}")
+
+            next_obs, reward, done, info = env.step(action_id)
+            total_step_reward += reward
+            session_state.total_reward += reward
+            session_state.observation = next_obs
+            session_state.last_info = info
+            session_state.done = bool(done)
+
+            step = GrlSokobanStepTrace(
+                action_id=action_id,
+                action_label=env.ACTION_LOOKUP[action_id],
+                reward=reward,
+                done=session_state.done,
+                info=info,
+            )
+            session_state.history.append(step)
+            steps.append(step)
+
+            if session_state.done:
+                break
+
+        return GrlSokobanStepResponse(
+            observation=session_state.observation,
+            reward=total_step_reward,
+            total_reward=session_state.total_reward,
+            done=session_state.done,
+            steps=steps,
+            history=list(session_state.history),  # Return full history for convenience
+        )
+
+    async def verify(self, request: Request, body: BaseVerifyRequest) -> GrlSokobanVerifyResponse:
+        session_id = request.session.get(SESSION_ID_KEY)
+        session_state = self.session_id_to_state.get(session_id)
+
+        success = False
+        reward = 0.0
+        if session_state is not None:
+            success = bool(session_state.last_info.get("success"))
+            reward = session_state.total_reward
+
+        if session_id in self.session_id_to_state:
+            try:
+                session_state.env.close()
+            except Exception:
+                pass
+            del self.session_id_to_state[session_id]
+
+        return GrlSokobanVerifyResponse(
+            **body.model_dump(),
+            reward=reward,
+            success=success,
+        )
+
+    @staticmethod
+    def _parse_action(action: Union[str, int], reverse_lookup: Dict[str, int]) -> int:
+        if isinstance(action, int):
+            return action
+
+        candidate = action.strip()
+        if candidate.lower() in reverse_lookup:
+            return reverse_lookup[candidate.lower()]
+
+        try:
+            return int(candidate)
+        except ValueError as exc:  # pragma: no cover - clarity around invalid input
+            raise HTTPException(status_code=400, detail=f"Unable to parse action: {action}") from exc
+
+
+if __name__ == "__main__":
+    GrlSokobanResourcesServer.run_webserver()
diff --git a/resources_servers/grl_sokoban/configs/grl_sokoban.yaml b/resources_servers/grl_sokoban/configs/grl_sokoban.yaml
new file mode 100644
index 000000000..ce33a525c
--- /dev/null
+++ b/resources_servers/grl_sokoban/configs/grl_sokoban.yaml
@@ -0,0 +1,28 @@
+grl_sokoban_resources_server:
+  resources_servers:
+    grl_sokoban:
+      entrypoint: app.py
+      domain: games
+      verified: true
+grl_sokoban_simple_agent:
+  responses_api_agents:
+    simple_agent:
+      entrypoint: app.py
+      max_steps: 10
+      count_tool_calls: true
+      resources_server:
+        type: resources_servers
+        name: grl_sokoban_resources_server
+      model_server:
+        type: responses_api_models
+        name: policy_model
+      datasets:
+      - name: example
+        type: example
+        jsonl_fpath: resources_servers/grl_sokoban/data/example.jsonl
+        num_repeats: 1
+        gitlab_identifier:
+          dataset_name: grl_sokoban
+          version: 0.0.1
+          artifact_fpath: example.jsonl
+        license: Apache 2.0
diff --git a/resources_servers/grl_sokoban/data/example.jsonl b/resources_servers/grl_sokoban/data/example.jsonl
new file mode 100644
index 000000000..aa6422c7a
--- /dev/null
+++ b/resources_servers/grl_sokoban/data/example.jsonl
@@ -0,0 +1,5 @@
+{"level_id": 1, "seed": 84810, "dim_room": [5, 5], "num_boxes": 1, "responses_create_params": {"max_tool_calls": 10, "input": [{"role": "developer", "content": "You are a Sokoban-solving assistant. IMPORTANT: First call the `step` tool with an empty array [] to see the initial puzzle state. Example: step({\"actions\": []}). The tool will return the board as a string with symbols: #=wall, _=floor, O=target, X=box, \u221a=box on target, P=player, S=player on target. Then continue calling `step` with valid actions (Up, Down, Left, Right) until the puzzle is solved or you run out of moves. At the end, respond with <answer>Action1 || Action2 || ...</answer> summarizing all moves you executed."}, {"role": "user", "content": "Call the step tool to see the puzzle, then solve it step by step."}], "tools": [{"name": "step", "type": "function", "description": "Execute Sokoban moves sequentially. Call with empty array [] to see current puzzle state without moving.", "strict": true, "parameters": {"type": "object", "properties": {"actions": {"type": "array", "items": {"type": "string"}, "description": "Sequence of actions e.g. ['Up', 'Right']. Use empty array [] to view current state."}}, "required": ["actions"], "additionalProperties": false}}]}}
+{"level_id": 2, "seed": 98293, "dim_room": [8, 8], "num_boxes": 1, "responses_create_params": {"max_tool_calls": 10, "input": [{"role": "developer", "content": "You are a Sokoban-solving assistant. IMPORTANT: First call the `step` tool with an empty array [] to see the initial puzzle state. Example: step({\"actions\": []}). The tool will return the board as a string with symbols: #=wall, _=floor, O=target, X=box, \u221a=box on target, P=player, S=player on target. Then continue calling `step` with valid actions (Up, Down, Left, Right) until the puzzle is solved or you run out of moves. At the end, respond with <answer>Action1 || Action2 || ...</answer> summarizing all moves you executed."}, {"role": "user", "content": "Call the step tool to see the puzzle, then solve it step by step."}], "tools": [{"name": "step", "type": "function", "description": "Execute Sokoban moves sequentially. Call with empty array [] to see current puzzle state without moving.", "strict": true, "parameters": {"type": "object", "properties": {"actions": {"type": "array", "items": {"type": "string"}, "description": "Sequence of actions e.g. ['Up', 'Right']. Use empty array [] to view current state."}}, "required": ["actions"], "additionalProperties": false}}]}}
+{"level_id": 3, "seed": 30450, "dim_room": [6, 6], "num_boxes": 1, "responses_create_params": {"max_tool_calls": 10, "input": [{"role": "developer", "content": "You are a Sokoban-solving assistant. IMPORTANT: First call the `step` tool with an empty array [] to see the initial puzzle state. Example: step({\"actions\": []}). The tool will return the board as a string with symbols: #=wall, _=floor, O=target, X=box, \u221a=box on target, P=player, S=player on target. Then continue calling `step` with valid actions (Up, Down, Left, Right) until the puzzle is solved or you run out of moves. At the end, respond with <answer>Action1 || Action2 || ...</answer> summarizing all moves you executed."}, {"role": "user", "content": "Call the step tool to see the puzzle, then solve it step by step."}], "tools": [{"name": "step", "type": "function", "description": "Execute Sokoban moves sequentially. Call with empty array [] to see current puzzle state without moving.", "strict": true, "parameters": {"type": "object", "properties": {"actions": {"type": "array", "items": {"type": "string"}, "description": "Sequence of actions e.g. ['Up', 'Right']. Use empty array [] to view current state."}}, "required": ["actions"], "additionalProperties": false}}]}}
+{"level_id": 4, "seed": 89987, "dim_room": [6, 7], "num_boxes": 1, "responses_create_params": {"max_tool_calls": 10, "input": [{"role": "developer", "content": "You are a Sokoban-solving assistant. IMPORTANT: First call the `step` tool with an empty array [] to see the initial puzzle state. Example: step({\"actions\": []}). The tool will return the board as a string with symbols: #=wall, _=floor, O=target, X=box, \u221a=box on target, P=player, S=player on target. Then continue calling `step` with valid actions (Up, Down, Left, Right) until the puzzle is solved or you run out of moves. At the end, respond with <answer>Action1 || Action2 || ...</answer> summarizing all moves you executed."}, {"role": "user", "content": "Call the step tool to see the puzzle, then solve it step by step."}], "tools": [{"name": "step", "type": "function", "description": "Execute Sokoban moves sequentially. Call with empty array [] to see current puzzle state without moving.", "strict": true, "parameters": {"type": "object", "properties": {"actions": {"type": "array", "items": {"type": "string"}, "description": "Sequence of actions e.g. ['Up', 'Right']. Use empty array [] to view current state."}}, "required": ["actions"], "additionalProperties": false}}]}}
+{"level_id": 5, "seed": 78785, "dim_room": [6, 4], "num_boxes": 1, "responses_create_params": {"max_tool_calls": 10, "input": [{"role": "developer", "content": "You are a Sokoban-solving assistant. IMPORTANT: First call the `step` tool with an empty array [] to see the initial puzzle state. Example: step({\"actions\": []}). The tool will return the board as a string with symbols: #=wall, _=floor, O=target, X=box, \u221a=box on target, P=player, S=player on target. Then continue calling `step` with valid actions (Up, Down, Left, Right) until the puzzle is solved or you run out of moves. At the end, respond with <answer>Action1 || Action2 || ...</answer> summarizing all moves you executed."}, {"role": "user", "content": "Call the step tool to see the puzzle, then solve it step by step."}], "tools": [{"name": "step", "type": "function", "description": "Execute Sokoban moves sequentially. Call with empty array [] to see current puzzle state without moving.", "strict": true, "parameters": {"type": "object", "properties": {"actions": {"type": "array", "items": {"type": "string"}, "description": "Sequence of actions e.g. ['Up', 'Right']. Use empty array [] to view current state."}}, "required": ["actions"], "additionalProperties": false}}]}}
diff --git a/resources_servers/grl_sokoban/data/example_metrics.json b/resources_servers/grl_sokoban/data/example_metrics.json
new file mode 100644
index 000000000..f522052d1
--- /dev/null
+++ b/resources_servers/grl_sokoban/data/example_metrics.json
@@ -0,0 +1,8 @@
+{
+    "name": "example",
+    "type": "example",
+    "jsonl_fpath": "resources_servers/grl_sokoban/data/example.jsonl",
+    "gitlab_identifier": null,
+    "license": "Apache 2.0",
+    "Number of examples": 5
+}
diff --git a/resources_servers/grl_sokoban/data/example_rollouts.jsonl b/resources_servers/grl_sokoban/data/example_rollouts.jsonl
new file mode 100644
index 000000000..059d42acb
--- /dev/null
+++ b/resources_servers/grl_sokoban/data/example_rollouts.jsonl
@@ -0,0 +1,5 @@
+{"responses_create_params": {"background": null, "include": null, "input": [{"content": "You are a Sokoban-solving assistant. IMPORTANT: First call the `step` tool with an empty array [] to see the initial puzzle state. Example: step({\"actions\": []}). The tool will return the board as a string with symbols: #=wall, _=floor, O=target, X=box, \u221a=box on target, P=player, S=player on target. Then continue calling `step` with valid actions (Up, Down, Left, Right) until the puzzle is solved or you run out of moves. 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I'll start by moving the player. \n\nFirst, I'll move the player right and then up to push the box.", "type": "output_text", "logprobs": []}], "role": "assistant", "status": "completed", "type": "message"}, {"arguments": "{\"actions\":[\"Right\"]}", "call_id": "call_UNxAFVy3iEpkJhpg55yzlQwb", "name": "step", "type": "function_call", "id": "fc_0d26a84ecf01a9f7006961960b50c881979bc96bd20f84722f", "status": "completed"}, {"arguments": "{\"actions\":[\"Up\"]}", "call_id": "call_uOetrSf4cgq0Wfmjfqg0hFnh", "name": "step", "type": "function_call", "id": "fc_0d26a84ecf01a9f7006961960b71a4819795d7f075d528946d", "status": "completed"}, {"call_id": "call_UNxAFVy3iEpkJhpg55yzlQwb", "output": "{\"observation\":\"######\\n##O__#\\n##___#\\n##_X##\\n##_P##\\n######\",\"reward\":-0.1,\"total_reward\":-0.1,\"done\":false,\"steps\":[{\"action_id\":4,\"action_label\":\"Right\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":false,\"action_is_valid\":true,\"success\":false}}],\"history\":[{\"action_id\":4,\"action_label\":\"Right\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":false,\"action_is_valid\":true,\"success\":false}}]}", "type": "function_call_output", "id": null, "status": null}, {"call_id": "call_uOetrSf4cgq0Wfmjfqg0hFnh", "output": "{\"observation\":\"######\\n##O__#\\n##_X_#\\n##_P##\\n##__##\\n######\",\"reward\":-0.1,\"total_reward\":-0.2,\"done\":false,\"steps\":[{\"action_id\":1,\"action_label\":\"Up\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}}],\"history\":[{\"action_id\":4,\"action_label\":\"Right\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":false,\"action_is_valid\":true,\"success\":false}},{\"action_id\":1,\"action_label\":\"Up\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}}]}", "type": "function_call_output", "id": null, "status": null}, {"id": "msg_0d26a84ecf01a9f7006961960cb77481979da3e415aa770802", "content": [{"annotations": [], "text": "The puzzle state is now:\n\n```\n######\n##O__#\n##_X_#\n##_P##\n##__##\n######\n```\n\nThe player has moved up below the box. Next, I'll move the player up again to push the box to the left.", "type": "output_text", "logprobs": []}], "role": "assistant", "status": "completed", "type": "message"}, {"arguments": "{\"actions\":[\"Up\"]}", "call_id": "call_cduGyAm8SEx6EdvfUAWBwh8C", "name": "step", "type": "function_call", "id": "fc_0d26a84ecf01a9f7006961960edfc88197a05c601220665a39", "status": "completed"}, {"call_id": "call_cduGyAm8SEx6EdvfUAWBwh8C", "output": "{\"observation\":\"######\\n##OX_#\\n##_P_#\\n##__##\\n##__##\\n######\",\"reward\":-0.1,\"total_reward\":-0.30000000000000004,\"done\":false,\"steps\":[{\"action_id\":1,\"action_label\":\"Up\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}}],\"history\":[{\"action_id\":4,\"action_label\":\"Right\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":false,\"action_is_valid\":true,\"success\":false}},{\"action_id\":1,\"action_label\":\"Up\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}},{\"action_id\":1,\"action_label\":\"Up\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}}]}", "type": "function_call_output", "id": null, "status": null}, {"id": "msg_0d26a84ecf01a9f7006961960fe0a0819781fb3e6ea68a9037", "content": [{"annotations": [], "text": "The puzzle state is now:\n\n```\n######\n##OX_#\n##_P_#\n##__##\n##__##\n######\n```\n\nThe box is on the target. 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Here are the moves I executed:\n\n<answer>Right || Up || Up || Left</answer>", "type": "output_text", "logprobs": []}], "role": "assistant", "status": "completed", "type": "message"}], "parallel_tool_calls": true, "temperature": 1.0, "tool_choice": "auto", "tools": [{"name": "step", "parameters": {"type": "object", "properties": {"actions": {"type": "array", "items": {"type": "string"}, "description": "Sequence of actions e.g. ['Up', 'Right']. Use empty array [] to view current state."}}, "required": ["actions"], "additionalProperties": false}, "strict": true, "type": "function", "description": "Execute Sokoban moves sequentially. 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+{"responses_create_params": {"background": null, "include": null, "input": [{"content": "You are a Sokoban-solving assistant. IMPORTANT: First call the `step` tool with an empty array [] to see the initial puzzle state. Example: step({\"actions\": []}). The tool will return the board as a string with symbols: #=wall, _=floor, O=target, X=box, \u221a=box on target, P=player, S=player on target. Then continue calling `step` with valid actions (Up, Down, Left, Right) until the puzzle is solved or you run out of moves. At the end, respond with <answer>Action1 || Action2 || ...</answer> summarizing all moves you executed.", "role": "developer", "type": "message"}, {"content": "Call the step tool to see the puzzle, then solve it step by step.", "role": "user", "type": "message"}], "instructions": null, "max_output_tokens": null, "max_tool_calls": 10, "metadata": null, "model": null, "parallel_tool_calls": true, "previous_response_id": null, "prompt": null, "reasoning": null, "service_tier": null, "store": null, "temperature": null, "text": null, "tool_choice": "auto", "tools": [{"name": "step", "parameters": {"type": "object", "properties": {"actions": {"type": "array", "items": {"type": "string"}, "description": "Sequence of actions e.g. ['Up', 'Right']. Use empty array [] to view current state."}}, "required": ["actions"], "additionalProperties": false}, "strict": true, "type": "function", "description": "Execute Sokoban moves sequentially. 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I'll start by moving the player towards the box and target.\n\n1. **Right** - Move the player to the right to align with the box.\n2. **Down** - Move the player down towards the box.\n3. **Down** - Move the player down next to the box.\n4. **Left** - Push the box to the left.\n\nContinuing with these steps, let me execute these actions.", "type": "output_text", "logprobs": []}], "role": "assistant", "status": "completed", "type": "message"}, {"arguments": "{\"actions\":[\"Right\"]}", "call_id": "call_XmYfXGwQiI1rftqrjWEmM0OC", "name": "step", "type": "function_call", "id": "fc_0a0449384ba97bd9006961960b89388195a9ebe739653fe81b", "status": "completed"}, {"arguments": "{\"actions\":[\"Down\"]}", "call_id": "call_rvDAcHCKMo2FbCVejiZpy20w", "name": "step", "type": "function_call", "id": "fc_0a0449384ba97bd9006961960ba0708195940945d4564df371", "status": "completed"}, {"arguments": "{\"actions\":[\"Down\"]}", "call_id": "call_njtJy7C92XC3LsIXMoRnjMuV", "name": "step", "type": "function_call", "id": "fc_0a0449384ba97bd9006961960bb0448195b0a9b5262bcabad2", "status": "completed"}, {"arguments": "{\"actions\":[\"Left\"]}", "call_id": "call_CnoX0hfOxhNZnSRIEz4pGiiI", "name": "step", "type": "function_call", "id": "fc_0a0449384ba97bd9006961960bbf148195b144d635358cb107", "status": "completed"}, {"call_id": "call_XmYfXGwQiI1rftqrjWEmM0OC", "output": "{\"observation\":\"######\\n##_P##\\n#_X###\\n#___##\\n#_O__#\\n######\",\"reward\":-0.1,\"total_reward\":-0.1,\"done\":false,\"steps\":[{\"action_id\":4,\"action_label\":\"Right\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}}],\"history\":[{\"action_id\":4,\"action_label\":\"Right\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}}]}", "type": "function_call_output", "id": null, "status": null}, {"call_id": "call_rvDAcHCKMo2FbCVejiZpy20w", "output": "{\"observation\":\"######\\n##_P##\\n#_X###\\n#___##\\n#_O__#\\n######\",\"reward\":-0.1,\"total_reward\":-0.2,\"done\":false,\"steps\":[{\"action_id\":2,\"action_label\":\"Down\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":false,\"action_is_valid\":true,\"success\":false}}],\"history\":[{\"action_id\":4,\"action_label\":\"Right\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}},{\"action_id\":2,\"action_label\":\"Down\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":false,\"action_is_valid\":true,\"success\":false}}]}", "type": "function_call_output", "id": null, "status": null}, {"call_id": "call_njtJy7C92XC3LsIXMoRnjMuV", "output": "{\"observation\":\"######\\n##_P##\\n#_X###\\n#___##\\n#_O__#\\n######\",\"reward\":-0.1,\"total_reward\":-0.30000000000000004,\"done\":false,\"steps\":[{\"action_id\":2,\"action_label\":\"Down\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":false,\"action_is_valid\":true,\"success\":false}}],\"history\":[{\"action_id\":4,\"action_label\":\"Right\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}},{\"action_id\":2,\"action_label\":\"Down\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":false,\"action_is_valid\":true,\"success\":false}},{\"action_id\":2,\"action_label\":\"Down\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":false,\"action_is_valid\":true,\"success\":false}}]}", "type": "function_call_output", "id": null, "status": null}, {"call_id": "call_CnoX0hfOxhNZnSRIEz4pGiiI", "output": "{\"observation\":\"######\\n##P_##\\n#_X###\\n#___##\\n#_O__#\\n######\",\"reward\":-0.1,\"total_reward\":-0.4,\"done\":false,\"steps\":[{\"action_id\":3,\"action_label\":\"Left\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}}],\"history\":[{\"action_id\":4,\"action_label\":\"Right\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}},{\"action_id\":2,\"action_label\":\"Down\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":false,\"action_is_valid\":true,\"success\":false}},{\"action_id\":2,\"action_label\":\"Down\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":false,\"action_is_valid\":true,\"success\":false}},{\"action_id\":3,\"action_label\":\"Left\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}}]}", "type": "function_call_output", "id": null, "status": null}, {"id": "msg_0a0449384ba97bd9006961960d089881958d2aa626b67ea8b1", "content": [{"annotations": [], "text": "It seems I attempted some ineffective movements. Let me correct this approach. Here's the current state:\n\n```\n######\n##P_##\n#_X###\n#___##\n#_O__#\n######\n```\n\nI'll try the following steps:\n\n1. **Down** - Move the player down to align horizontally with the box.\n2. **Down** - Continue down towards the target row.\n3. **Right** - Move towards the target.\n4. **Up** - Push the box up onto the target.\n\nLet me execute these actions.", "type": "output_text", "logprobs": []}], "role": "assistant", "status": "completed", "type": "message"}, {"arguments": "{\"actions\":[\"Down\"]}", "call_id": "call_X2TKOejP1djIn671I97B9ymT", "name": "step", "type": "function_call", "id": "fc_0a0449384ba97bd90069619610d1748195a224e2917b9f4bfb", "status": "completed"}, {"call_id": "call_X2TKOejP1djIn671I97B9ymT", "output": "{\"observation\":\"######\\n##__##\\n#_P###\\n#_X_##\\n#_O__#\\n######\",\"reward\":-0.1,\"total_reward\":-0.5,\"done\":false,\"steps\":[{\"action_id\":2,\"action_label\":\"Down\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}}],\"history\":[{\"action_id\":4,\"action_label\":\"Right\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}},{\"action_id\":2,\"action_label\":\"Down\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":false,\"action_is_valid\":true,\"success\":false}},{\"action_id\":2,\"action_label\":\"Down\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":false,\"action_is_valid\":true,\"success\":false}},{\"action_id\":3,\"action_label\":\"Left\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}},{\"action_id\":2,\"action_label\":\"Down\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}}]}", "type": "function_call_output", "id": null, "status": null}, {"arguments": "{\"actions\":[\"Right\"]}", "call_id": "call_eXOrlSVcLNebejR9FHaR8x8A", "name": "step", "type": "function_call", "id": "fc_0a0449384ba97bd90069619611d2ec8195b4ac0c5ff7777721", "status": "completed"}, {"call_id": "call_eXOrlSVcLNebejR9FHaR8x8A", "output": "{\"observation\":\"######\\n##__##\\n#_P###\\n#_X_##\\n#_O__#\\n######\",\"reward\":-0.1,\"total_reward\":-0.6,\"done\":false,\"steps\":[{\"action_id\":4,\"action_label\":\"Right\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":false,\"action_is_valid\":true,\"success\":false}}],\"history\":[{\"action_id\":4,\"action_label\":\"Right\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}},{\"action_id\":2,\"action_label\":\"Down\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":false,\"action_is_valid\":true,\"success\":false}},{\"action_id\":2,\"action_label\":\"Down\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":false,\"action_is_valid\":true,\"success\":false}},{\"action_id\":3,\"action_label\":\"Left\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}},{\"action_id\":2,\"action_label\":\"Down\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}},{\"action_id\":4,\"action_label\":\"Right\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":false,\"action_is_valid\":true,\"success\":false}}]}", "type": "function_call_output", "id": null, "status": null}, {"id": "msg_0a0449384ba97bd90069619612be708195982df252335f1b28", "content": [{"annotations": [], "text": "I need to adjust my approach. Let's move more efficiently towards the solution:\n\n1. **Left** - Get the player to a better position to push the box.\n2. **Down** - Move to be vertically aligned with the target.\n3. **Right** - Move towards the box.\n4. **Up** - Push the box onto the target.\n\nExecuting these actions now.", "type": "output_text", "logprobs": []}], "role": "assistant", "status": "completed", "type": "message"}, {"arguments": "{\"actions\":[\"Left\"]}", "call_id": "call_viKVOtZvs23nbis0ypNBNJLS", "name": "step", "type": "function_call", "id": "fc_0a0449384ba97bd9006961961429948195981d18cce2f6ce07", "status": "completed"}, {"call_id": "call_viKVOtZvs23nbis0ypNBNJLS", "output": "{\"observation\":\"######\\n##__##\\n#P_###\\n#_X_##\\n#_O__#\\n######\",\"reward\":-0.1,\"total_reward\":-0.7,\"done\":false,\"steps\":[{\"action_id\":3,\"action_label\":\"Left\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}}],\"history\":[{\"action_id\":4,\"action_label\":\"Right\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}},{\"action_id\":2,\"action_label\":\"Down\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":false,\"action_is_valid\":true,\"success\":false}},{\"action_id\":2,\"action_label\":\"Down\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":false,\"action_is_valid\":true,\"success\":false}},{\"action_id\":3,\"action_label\":\"Left\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}},{\"action_id\":2,\"action_label\":\"Down\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}},{\"action_id\":4,\"action_label\":\"Right\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":false,\"action_is_valid\":true,\"success\":false}},{\"action_id\":3,\"action_label\":\"Left\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}}]}", 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+{"responses_create_params": {"background": null, "include": null, "input": [{"content": "You are a Sokoban-solving assistant. IMPORTANT: First call the `step` tool with an empty array [] to see the initial puzzle state. Example: step({\"actions\": []}). The tool will return the board as a string with symbols: #=wall, _=floor, O=target, X=box, \u221a=box on target, P=player, S=player on target. Then continue calling `step` with valid actions (Up, Down, Left, Right) until the puzzle is solved or you run out of moves. At the end, respond with <answer>Action1 || Action2 || ...</answer> summarizing all moves you executed.", "role": "developer", "type": "message"}, {"content": "Call the step tool to see the puzzle, then solve it step by step.", "role": "user", "type": "message"}], "instructions": null, "max_output_tokens": null, "max_tool_calls": 10, "metadata": null, "model": null, "parallel_tool_calls": true, "previous_response_id": null, "prompt": null, "reasoning": null, "service_tier": null, "store": null, "temperature": null, "text": null, "tool_choice": "auto", "tools": [{"name": "step", "parameters": {"type": "object", "properties": {"actions": {"type": "array", "items": {"type": "string"}, "description": "Sequence of actions e.g. ['Up', 'Right']. Use empty array [] to view current state."}}, "required": ["actions"], "additionalProperties": false}, "strict": true, "type": "function", "description": "Execute Sokoban moves sequentially. Call with empty array [] to see current puzzle state without moving."}], "top_logprobs": null, "top_p": null, "truncation": null, "user": null, "stream": null}, "response": {"id": "resp_0bd77eb61e5890d70069619617d998819f9dcf620d1ca22841", "created_at": 1768003095.0, "error": null, "incomplete_details": null, "instructions": null, "metadata": {}, "model": "gpt-4o-2024-08-06", "object": "response", "output": [{"arguments": "{\"actions\":[]}", "call_id": "call_btpp7r50e5wVNdT1xiOUmLMw", "name": "step", "type": "function_call", "id": "fc_0bd77eb61e5890d70069619608ad6c819fb403a70e5aa70dca", "status": "completed"}, {"call_id": "call_btpp7r50e5wVNdT1xiOUmLMw", "output": "{\"observation\":\"######\\n###P_#\\n##_X_#\\n#____#\\n#_O__#\\n######\",\"reward\":0.0,\"total_reward\":0.0,\"done\":false,\"steps\":[],\"history\":[]}", "type": "function_call_output", "id": null, "status": null}, {"id": "msg_0bd77eb61e5890d700696196096ad8819f80b7decdca2c227c", "content": [{"annotations": [], "text": "Here's the initial puzzle state:\n\n```\n######\n###P_#\n##_X_#\n#____#\n#_O__#\n######\n```\n\n- `#`: Wall\n- `_`: Floor\n- `O`: Target\n- `X`: Box\n- `P`: Player\n\nLet's start solving the puzzle. \n\nFirst, move the player towards the box:\n\n1. **Down**\n\n", "type": "output_text", "logprobs": []}], "role": "assistant", "status": "completed", "type": "message"}, {"arguments": "{\"actions\":[\"Down\"]}", "call_id": "call_IFNXHc5GYTsSWBGTiJlv3Z7y", "name": "step", "type": "function_call", "id": "fc_0bd77eb61e5890d7006961960ac714819facf3e7c8878b263c", "status": "completed"}, {"call_id": "call_IFNXHc5GYTsSWBGTiJlv3Z7y", "output": "{\"observation\":\"######\\n###__#\\n##_P_#\\n#__X_#\\n#_O__#\\n######\",\"reward\":-0.1,\"total_reward\":-0.1,\"done\":false,\"steps\":[{\"action_id\":2,\"action_label\":\"Down\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}}],\"history\":[{\"action_id\":2,\"action_label\":\"Down\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}}]}", "type": "function_call_output", "id": null, "status": null}, {"id": "msg_0bd77eb61e5890d7006961960c276c819fa3a57e03b46e70d2", "content": [{"annotations": [], "text": "Player moved down:\n\n```\n######\n###__#\n##_P_#\n#__X_#\n#_O__#\n######\n```\n\nNext, move the player towards the box to push it:\n\n2. **Down**\n\n", "type": "output_text", "logprobs": []}], "role": "assistant", "status": "completed", "type": "message"}, {"arguments": "{\"actions\":[\"Down\"]}", "call_id": "call_zUP7gLjFlQKIbGMRUUkfizFy", "name": "step", "type": "function_call", "id": "fc_0bd77eb61e5890d7006961960d29dc819fa180fd964ecb155c", "status": "completed"}, {"call_id": "call_zUP7gLjFlQKIbGMRUUkfizFy", "output": "{\"observation\":\"######\\n###__#\\n##___#\\n#__P_#\\n#_OX_#\\n######\",\"reward\":-0.1,\"total_reward\":-0.2,\"done\":false,\"steps\":[{\"action_id\":2,\"action_label\":\"Down\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}}],\"history\":[{\"action_id\":2,\"action_label\":\"Down\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}},{\"action_id\":2,\"action_label\":\"Down\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}}]}", "type": "function_call_output", "id": null, "status": null}, {"id": "msg_0bd77eb61e5890d7006961960e3ff0819f83498c1bc2f41988", "content": [{"annotations": [], "text": "Player moved down:\n\n```\n######\n###__#\n##___#\n#__P_#\n#_OX_#\n######\n```\n\nNow, push the box to the right onto the target:\n\n3. **Right**\n\n", "type": "output_text", "logprobs": []}], "role": "assistant", "status": "completed", "type": "message"}, {"arguments": "{\"actions\":[\"Right\"]}", "call_id": "call_YlKTj70bEsVBos86esWdpewP", "name": "step", "type": "function_call", "id": "fc_0bd77eb61e5890d7006961960f298c819fb8e1f33e9dc00549", "status": "completed"}, {"call_id": "call_YlKTj70bEsVBos86esWdpewP", "output": "{\"observation\":\"######\\n###__#\\n##___#\\n#___P#\\n#_OX_#\\n######\",\"reward\":-0.1,\"total_reward\":-0.30000000000000004,\"done\":false,\"steps\":[{\"action_id\":4,\"action_label\":\"Right\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}}],\"history\":[{\"action_id\":2,\"action_label\":\"Down\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}},{\"action_id\":2,\"action_label\":\"Down\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}},{\"action_id\":4,\"action_label\":\"Right\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}}]}", "type": "function_call_output", "id": null, "status": null}, {"id": "msg_0bd77eb61e5890d700696196104eac819f9816eb029c809230", "content": [{"annotations": [], "text": "Player moved right:\n\n```\n######\n###__#\n##___#\n#___P#\n#_OX_#\n######\n```\n\nContinue pushing the box to the right:\n\n4. **Right**\n\n", "type": "output_text", "logprobs": []}], "role": "assistant", "status": "completed", "type": "message"}, {"arguments": "{\"actions\":[\"Right\"]}", "call_id": "call_fV9yrvPImoOGbdiKPGhhwaqc", "name": "step", "type": "function_call", "id": "fc_0bd77eb61e5890d700696196116770819f9462e5d75102cb35", "status": "completed"}, {"call_id": "call_fV9yrvPImoOGbdiKPGhhwaqc", "output": "{\"observation\":\"######\\n###__#\\n##___#\\n#___P#\\n#_OX_#\\n######\",\"reward\":-0.1,\"total_reward\":-0.4,\"done\":false,\"steps\":[{\"action_id\":4,\"action_label\":\"Right\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":false,\"action_is_valid\":true,\"success\":false}}],\"history\":[{\"action_id\":2,\"action_label\":\"Down\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}},{\"action_id\":2,\"action_label\":\"Down\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}},{\"action_id\":4,\"action_label\":\"Right\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}},{\"action_id\":4,\"action_label\":\"Right\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":false,\"action_is_valid\":true,\"success\":false}}]}", "type": "function_call_output", "id": null, "status": null}, {"id": "msg_0bd77eb61e5890d700696196125e4c819f96a4a84c00b5ac96", "content": [{"annotations": [], "text": "The player can't move further right, as there's a wall. Let's adjust our approach by moving down and then attempting another move:\n\n5. **Down**\n\n", "type": "output_text", "logprobs": []}], "role": "assistant", "status": "completed", "type": "message"}, {"arguments": "{\"actions\":[\"Down\"]}", "call_id": "call_Pu4g0L9fiaqt4ijal49MNQKD", "name": "step", "type": "function_call", "id": "fc_0bd77eb61e5890d70069619613806c819f90234e4bda3f71c6", "status": "completed"}, {"call_id": "call_Pu4g0L9fiaqt4ijal49MNQKD", "output": "{\"observation\":\"######\\n###__#\\n##___#\\n#____#\\n#_OXP#\\n######\",\"reward\":-0.1,\"total_reward\":-0.5,\"done\":false,\"steps\":[{\"action_id\":2,\"action_label\":\"Down\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}}],\"history\":[{\"action_id\":2,\"action_label\":\"Down\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}},{\"action_id\":2,\"action_label\":\"Down\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}},{\"action_id\":4,\"action_label\":\"Right\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}},{\"action_id\":4,\"action_label\":\"Right\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":false,\"action_is_valid\":true,\"success\":false}},{\"action_id\":2,\"action_label\":\"Down\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}}]}", "type": "function_call_output", "id": null, "status": null}, {"id": "msg_0bd77eb61e5890d7006961961452ac819fbe7ee02030416759", "content": [{"annotations": [], "text": "Player moved down:\n\n```\n######\n###__#\n##___#\n#____#\n#_OXP#\n######\n```\n\nNow, let's push the box onto the target:\n\n6. **Left**\n\n", "type": "output_text", "logprobs": []}], "role": "assistant", "status": "completed", "type": "message"}, {"arguments": "{\"actions\":[\"Left\"]}", "call_id": "call_PF5bepeT0mqHKUoEQUHfHhXn", "name": "step", "type": "function_call", "id": "fc_0bd77eb61e5890d700696196178044819f9cecef2d3c2dcd92", "status": "completed"}, {"call_id": "call_PF5bepeT0mqHKUoEQUHfHhXn", "output": "{\"observation\":\"######\\n###__#\\n##___#\\n#____#\\n#_\u221aP_#\\n######\",\"reward\":10.9,\"total_reward\":10.4,\"done\":true,\"steps\":[{\"action_id\":3,\"action_label\":\"Left\",\"reward\":10.9,\"done\":true,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":true}}],\"history\":[{\"action_id\":2,\"action_label\":\"Down\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}},{\"action_id\":2,\"action_label\":\"Down\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}},{\"action_id\":4,\"action_label\":\"Right\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}},{\"action_id\":4,\"action_label\":\"Right\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":false,\"action_is_valid\":true,\"success\":false}},{\"action_id\":2,\"action_label\":\"Down\",\"reward\":-0.1,\"done\":false,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":false}},{\"action_id\":3,\"action_label\":\"Left\",\"reward\":10.9,\"done\":true,\"info\":{\"action_is_effective\":true,\"action_is_valid\":true,\"success\":true}}]}", "type": "function_call_output", "id": null, "status": null}, {"id": "msg_0bd77eb61e5890d700696196194d18819fab92f391bf8cf564", "content": [{"annotations": [], "text": "The box is successfully placed on the target:\n\n```\n######\n###__#\n##___#\n#____#\n#_\u221aP_#\n######\n```\n\nThe puzzle is solved!\n\n<answer>Down || Down || Right || Right || Down || Left</answer>", "type": "output_text", "logprobs": []}], "role": "assistant", "status": "completed", "type": "message"}], "parallel_tool_calls": true, "temperature": 1.0, "tool_choice": "auto", "tools": [{"name": "step", "parameters": {"type": "object", "properties": {"actions": {"type": "array", "items": {"type": "string"}, "description": "Sequence of actions e.g. ['Up', 'Right']. Use empty array [] to view current state."}}, "required": ["actions"], "additionalProperties": false}, "strict": true, "type": "function", "description": "Execute Sokoban moves sequentially. Call with empty array [] to see current puzzle state without moving."}], "top_p": 1.0, "background": false, "conversation": null, "max_output_tokens": null, "max_tool_calls": 10, "previous_response_id": null, "prompt": null, "prompt_cache_key": null, "reasoning": {"effort": null, "generate_summary": null, "summary": null}, "safety_identifier": null, "service_tier": "default", "status": "completed", "text": {"format": {"type": "text"}, "verbosity": "medium"}, "top_logprobs": 0, "truncation": "disabled", "usage": {"input_tokens": 2191, "input_tokens_details": {"cached_tokens": 1792}, "output_tokens": 56, "output_tokens_details": {"reasoning_tokens": 0}, "total_tokens": 2247}, "user": null, "billing": {"payer": "developer"}, "completed_at": 1768003098, "frequency_penalty": 0.0, "presence_penalty": 0.0, "prompt_cache_retention": null, "store": true}, "reward": 10.4, "success": true}
diff --git a/resources_servers/grl_sokoban/data/train_metrics.json b/resources_servers/grl_sokoban/data/train_metrics.json
new file mode 100644
index 000000000..681e3f0da
--- /dev/null
+++ b/resources_servers/grl_sokoban/data/train_metrics.json
@@ -0,0 +1,77 @@
+{
+    "name": "train",
+    "type": "train",
+    "jsonl_fpath": "resources_servers/grl_sokoban/data/train.jsonl",
+    "num_repeats": 1,
+    "gitlab_identifier": {
+        "dataset_name": "grl_sokoban",
+        "version": "0.0.1",
+        "artifact_fpath": "grl_sokoban/train.jsonl"
+    },
+    "license": "Apache 2.0",
+    "Number of examples": 1600,
+    "Number of tools": {
+        "Total # non-null values": 1600,
+        "Average": 1.0,
+        "Min": 1.0,
+        "Max": 1.0,
+        "Median": 1.0,
+        "Standard deviation": 0.0
+    },
+    "Json-dumped number of words (proxy for token count)": {
+        "Total # non-null values": 1600,
+        "Average": 160.0,
+        "Min": 160.0,
+        "Max": 160.0,
+        "Median": 160.0,
+        "Standard deviation": 0.0
+    },
+    "Number of turns": {
+        "Total # non-null values": 1600,
+        "Average": 1.0,
+        "Min": 1.0,
+        "Max": 1.0,
+        "Median": 1.0,
+        "Standard deviation": 0.0
+    },
+    "Temperature": {
+        "Total # non-null values": 0,
+        "Average": 0.0,
+        "Min": 0.0,
+        "Max": 0.0,
+        "Median": 0.0,
+        "Standard deviation": 0.0
+    },
+    "level_id": {
+        "Total # non-null values": 1600,
+        "Average": 1001.68,
+        "Min": 1.0,
+        "Max": 2000.0,
+        "Median": 997.75,
+        "Standard deviation": 574.92
+    },
+    "seed": {
+        "Total # non-null values": 1600,
+        "Average": 604177.21,
+        "Min": 11500.0,
+        "Max": 1186049.0,
+        "Median": 610086.33,
+        "Standard deviation": 295565.88
+    },
+    "dim_room": {
+        "Total # non-null values": 3200,
+        "Average": 6.0,
+        "Min": 6.0,
+        "Max": 6.0,
+        "Median": 6.0,
+        "Standard deviation": 0.0
+    },
+    "num_boxes": {
+        "Total # non-null values": 1600,
+        "Average": 1.0,
+        "Min": 1.0,
+        "Max": 1.0,
+        "Median": 1.0,
+        "Standard deviation": 0.0
+    }
+}
\ No newline at end of file
diff --git a/resources_servers/grl_sokoban/data/validation_metrics.json b/resources_servers/grl_sokoban/data/validation_metrics.json
new file mode 100644
index 000000000..49016db66
--- /dev/null
+++ b/resources_servers/grl_sokoban/data/validation_metrics.json
@@ -0,0 +1,77 @@
+{
+    "name": "validation",
+    "type": "validation",
+    "jsonl_fpath": "resources_servers/grl_sokoban/data/validation.jsonl",
+    "num_repeats": 1,
+    "gitlab_identifier": {
+        "dataset_name": "grl_sokoban",
+        "version": "0.0.1",
+        "artifact_fpath": "grl_sokoban/validation.jsonl"
+    },
+    "license": "Apache 2.0",
+    "Number of examples": 400,
+    "Number of tools": {
+        "Total # non-null values": 400,
+        "Average": 1.0,
+        "Min": 1.0,
+        "Max": 1.0,
+        "Median": 1.0,
+        "Standard deviation": 0.0
+    },
+    "Json-dumped number of words (proxy for token count)": {
+        "Total # non-null values": 400,
+        "Average": 160.0,
+        "Min": 160.0,
+        "Max": 160.0,
+        "Median": 160.0,
+        "Standard deviation": 0.0
+    },
+    "Number of turns": {
+        "Total # non-null values": 400,
+        "Average": 1.0,
+        "Min": 1.0,
+        "Max": 1.0,
+        "Median": 1.0,
+        "Standard deviation": 0.0
+    },
+    "Temperature": {
+        "Total # non-null values": 0,
+        "Average": 0.0,
+        "Min": 0.0,
+        "Max": 0.0,
+        "Median": 0.0,
+        "Standard deviation": 0.0
+    },
+    "level_id": {
+        "Total # non-null values": 400,
+        "Average": 995.79,
+        "Min": 2.0,
+        "Max": 1999.0,
+        "Median": 1015.25,
+        "Standard deviation": 588.39
+    },
+    "seed": {
+        "Total # non-null values": 400,
+        "Average": 594103.9,
+        "Min": 24675.0,
+        "Max": 1175285.0,
+        "Median": 593245.0,
+        "Standard deviation": 289588.18
+    },
+    "dim_room": {
+        "Total # non-null values": 800,
+        "Average": 6.0,
+        "Min": 6.0,
+        "Max": 6.0,
+        "Median": 6.0,
+        "Standard deviation": 0.0
+    },
+    "num_boxes": {
+        "Total # non-null values": 400,
+        "Average": 1.0,
+        "Min": 1.0,
+        "Max": 1.0,
+        "Median": 1.0,
+        "Standard deviation": 0.0
+    }
+}
\ No newline at end of file
diff --git a/resources_servers/grl_sokoban/generate_test_examples.py b/resources_servers/grl_sokoban/generate_test_examples.py
new file mode 100644
index 000000000..2d578018a
--- /dev/null
+++ b/resources_servers/grl_sokoban/generate_test_examples.py
@@ -0,0 +1,166 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Generate test examples for GRL Sokoban environment.
+
+This script creates diverse test examples with varying seeds and room dimensions.
+"""
+
+import json
+import random
+from pathlib import Path
+from typing import Any, Dict, List
+
+
+def generate_sokoban_example(level_id: int, seed: int, dim_room: List[int], num_boxes: int) -> Dict[str, Any]:
+    """Generate a single Sokoban test example in the expected JSONL format.
+
+    Args:
+        level_id: Unique identifier for the level
+        seed: Random seed for reproducible level generation
+        dim_room: Room dimensions as [width, height]
+        num_boxes: Number of boxes in the puzzle
+
+    Returns:
+        Dictionary containing the level configuration and prompt
+    """
+    return {
+        "level_id": level_id,
+        "seed": seed,
+        "dim_room": dim_room,
+        "num_boxes": num_boxes,
+        "responses_create_params": {
+            "max_tool_calls": 10,
+            "input": [
+                {
+                    "role": "developer",
+                    "content": 'You are a Sokoban-solving assistant. IMPORTANT: First call the `step` tool with an empty array [] to see the initial puzzle state. Example: step({"actions": []}). The tool will return the board as a string with symbols: #=wall, _=floor, O=target, X=box, √=box on target, P=player, S=player on target. Then continue calling `step` with valid actions (Up, Down, Left, Right) until the puzzle is solved or you run out of moves. At the end, respond with <answer>Action1 || Action2 || ...</answer> summarizing all moves you executed.',
+                },
+                {"role": "user", "content": "Call the step tool to see the puzzle, then solve it step by step."},
+            ],
+            "tools": [
+                {
+                    "name": "step",
+                    "type": "function",
+                    "description": "Execute Sokoban moves sequentially. Call with empty array [] to see current puzzle state without moving.",
+                    "strict": True,
+                    "parameters": {
+                        "type": "object",
+                        "properties": {
+                            "actions": {
+                                "type": "array",
+                                "items": {"type": "string"},
+                                "description": "Sequence of actions e.g. ['Up', 'Right']. Use empty array [] to view current state.",
+                            }
+                        },
+                        "required": ["actions"],
+                        "additionalProperties": False,
+                    },
+                }
+            ],
+        },
+    }
+
+
+def generate_test_examples(num_examples: int = 500, output_file: str = "data/test_examples.jsonl") -> None:
+    """Generate diverse test examples for Sokoban environment.
+
+    Args:
+        num_examples: Number of examples to generate (default: 500)
+        output_file: Output JSONL file path
+    """
+    examples = []
+
+    # Define parameter ranges for diversity
+    room_sizes = [
+        [4, 4],  # Tiny square
+        [5, 5],  # Small square
+        [6, 6],  # Medium square
+        [7, 7],  # Large square
+        [8, 8],  # Extra large square
+        [4, 6],  # Narrow tall
+        [6, 4],  # Wide short
+        [5, 6],  # Small tall
+        [6, 5],  # Small wide
+        [5, 7],  # Medium tall
+        [7, 5],  # Medium wide
+        [6, 7],  # Large tall
+        [7, 6],  # Large wide
+    ]
+
+    # Primarily use 1 box (most common), but include some harder puzzles
+    num_boxes_options = [1, 1, 1, 1, 1, 2, 2, 3]  # Weighted toward 1 box
+
+    # Generate diverse examples
+    for i in range(num_examples):
+        level_id = i + 1
+
+        # Use level_id as base for seed to ensure reproducibility but diversity
+        seed = random.randint(1000, 99999) + i * 97  # Prime offset for better distribution
+
+        # Cycle through room sizes with some randomness
+        dim_room = random.choice(room_sizes)
+
+        # Most puzzles should have 1 box, some have more
+        num_boxes = random.choice(num_boxes_options)
+
+        # Ensure room is large enough for boxes
+        min_room_size = dim_room[0] * dim_room[1]
+        if num_boxes >= min_room_size // 3:
+            num_boxes = 1  # Fall back to 1 box if room is too small
+
+        example = generate_sokoban_example(level_id, seed, dim_room, num_boxes)
+        examples.append(example)
+
+    # Write to JSONL file
+    output_path = Path(__file__).parent / output_file
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+
+    with open(output_path, "w") as f:
+        for example in examples:
+            f.write(json.dumps(example) + "\n")
+
+    print(f"Generated {num_examples} test examples")
+    print(f"Output file: {output_path}")
+    print("\nParameter distribution:")
+    print(f"  Room sizes: {sorted(set(tuple(e['dim_room']) for e in examples))}")
+    print("  Num boxes distribution:")
+    box_counts = {}
+    for e in examples:
+        nb = e["num_boxes"]
+        box_counts[nb] = box_counts.get(nb, 0) + 1
+    for nb in sorted(box_counts.keys()):
+        print(f"    {nb} boxes: {box_counts[nb]} examples ({100 * box_counts[nb] / num_examples:.1f}%)")
+    print(f"  Seed range: {min(e['seed'] for e in examples)} - {max(e['seed'] for e in examples)}")
+
+
+if __name__ == "__main__":
+    import argparse
+
+    parser = argparse.ArgumentParser(description="Generate Sokoban test examples")
+    parser.add_argument("--num-examples", type=int, default=500, help="Number of examples to generate (default: 500)")
+    parser.add_argument(
+        "--output",
+        type=str,
+        default="data/test_examples.jsonl",
+        help="Output JSONL file path (default: data/test_examples.jsonl)",
+    )
+    parser.add_argument("--seed", type=int, default=42, help="Random seed for generation (default: 42)")
+
+    args = parser.parse_args()
+
+    # Set random seed for reproducibility
+    random.seed(args.seed)
+
+    generate_test_examples(args.num_examples, args.output)
diff --git a/resources_servers/grl_sokoban/requirements.txt b/resources_servers/grl_sokoban/requirements.txt
new file mode 100644
index 000000000..0e29469f1
--- /dev/null
+++ b/resources_servers/grl_sokoban/requirements.txt
@@ -0,0 +1,10 @@
+-e nemo-gym[dev] @ ../../
+gymnasium==1.1.1
+gym-sokoban==0.0.6
+numpy>=1.26
+scipy>=1.11
+imageio>=2.31
+pillow>=10
+pyyaml>=6.0
+pkgconfig>=1.5
+setuptools>=70
diff --git a/resources_servers/grl_sokoban/sokoban_env/__init__.py b/resources_servers/grl_sokoban/sokoban_env/__init__.py
new file mode 100644
index 000000000..12800d204
--- /dev/null
+++ b/resources_servers/grl_sokoban/sokoban_env/__init__.py
@@ -0,0 +1,6 @@
+"""Local Sokoban environment implementation."""
+
+from .sokoban_env import SokobanEnv
+
+
+__all__ = ["SokobanEnv"]
diff --git a/resources_servers/grl_sokoban/sokoban_env/generation.py b/resources_servers/grl_sokoban/sokoban_env/generation.py
new file mode 100644
index 000000000..af84c0ce3
--- /dev/null
+++ b/resources_servers/grl_sokoban/sokoban_env/generation.py
@@ -0,0 +1,345 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Sokoban level generation utilities.
+
+This module is adapted from the ``gym_sokoban`` project and GRL's fork of the
+same utilities. It produces solvable single-box Sokoban rooms suitable for
+text-based rendering.
+
+This implementation is based on code from https://github.com/lmgame-org/lmenv,
+developed in collaboration with NVIDIA.
+"""
+
+from __future__ import annotations
+
+import marshal
+import random
+from collections import deque
+from typing import Dict, List, Tuple
+
+import numpy as np
+
+
+# Constants for room generation
+TYPE_LOOKUP = {
+    0: "wall",
+    1: "empty space",
+    2: "box target",
+    3: "box on target",
+    4: "box not on target",
+    5: "player",
+}
+
+ACTION_LOOKUP_INTERNAL = {
+    0: "push up",
+    1: "push down",
+    2: "push left",
+    3: "push right",
+    4: "move up",
+    5: "move down",
+    6: "move left",
+    7: "move right",
+}
+
+# Moves are mapped to coordinate changes as follows
+# 0: Move up, 1: Move down, 2: Move left, 3: Move right
+CHANGE_COORDINATES = {0: (-1, 0), 1: (1, 0), 2: (0, -1), 3: (0, 1)}
+
+
+def generate_room(
+    dim: Tuple[int, int] = (13, 13),
+    p_change_directions: float = 0.35,
+    num_steps: int = 25,
+    num_boxes: int = 3,
+    tries: int = 4,
+    second_player: bool = False,
+    search_depth: int = 100,
+):
+    """Generate a Sokoban room represented as integer matrices."""
+
+    room_state = np.zeros(shape=dim)
+    room_structure = np.zeros(shape=dim)
+
+    for _ in range(tries):
+        room = room_topology_generation(dim, p_change_directions, num_steps)
+        room = place_boxes_and_player(room, num_boxes=num_boxes, second_player=second_player)
+
+        room_structure = np.copy(room)
+        room_structure[room_structure == 5] = 1
+
+        room_state = room.copy()
+        room_state[room_state == 2] = 4
+
+        room_state, box_mapping, action_sequence = reverse_playing(room_state, room_structure, search_depth)
+        room_state[room_state == 3] = 4
+
+        if box_displacement_score(box_mapping) > 0:
+            break
+
+    if box_displacement_score(box_mapping) == 0:
+        raise RuntimeWarning("Generated Model with score == 0")
+
+    move_probability = 0.8 if box_displacement_score(box_mapping) == 1 else 0.5
+    room_state = add_random_player_movement(
+        room_state,
+        room_structure,
+        move_probability=move_probability,
+        continue_probability=0.5,
+        max_steps=3,
+    )
+
+    return room_structure, room_state, box_mapping, action_sequence
+
+
+def room_topology_generation(dim: Tuple[int, int] = (10, 10), p_change_directions: float = 0.35, num_steps: int = 15):
+    dim_x, dim_y = dim
+
+    masks = [
+        [[0, 0, 0], [1, 1, 1], [0, 0, 0]],
+        [[0, 1, 0], [0, 1, 0], [0, 1, 0]],
+        [[0, 0, 0], [1, 1, 0], [0, 1, 0]],
+        [[0, 0, 0], [1, 1, 0], [1, 1, 0]],
+        [[0, 0, 0], [0, 1, 1], [0, 1, 0]],
+    ]
+
+    directions = [(1, 0), (0, 1), (-1, 0), (0, -1)]
+    direction = random.sample(directions, 1)[0]
+
+    position = np.array([random.randint(1, dim_x - 1), random.randint(1, dim_y - 1)])
+
+    level = np.zeros(dim, dtype=int)
+
+    for _ in range(num_steps):
+        if random.random() < p_change_directions:
+            direction = random.sample(directions, 1)[0]
+
+        position = position + direction
+        position[0] = max(min(position[0], dim_x - 2), 1)
+        position[1] = max(min(position[1], dim_y - 2), 1)
+
+        mask = random.sample(masks, 1)[0]
+        mask_start = position - 1
+        level[mask_start[0] : mask_start[0] + 3, mask_start[1] : mask_start[1] + 3] += mask
+
+    level[level > 0] = 1
+    level[:, [0, dim_y - 1]] = 0
+    level[[0, dim_x - 1], :] = 0
+
+    return level
+
+
+def place_boxes_and_player(room: np.ndarray, num_boxes: int, second_player: bool):
+    possible_positions = np.where(room == 1)
+    num_possible_positions = possible_positions[0].shape[0]
+    num_players = 2 if second_player else 1
+
+    if num_possible_positions <= num_boxes + num_players:
+        raise RuntimeError(
+            "Not enough free spots ({}) to place {} player(s) and {} boxes.".format(
+                num_possible_positions, num_players, num_boxes
+            )
+        )
+
+    ind = np.random.randint(num_possible_positions)
+    player_position = possible_positions[0][ind], possible_positions[1][ind]
+    room[player_position] = 5
+
+    if second_player:
+        ind = np.random.randint(num_possible_positions)
+        player_position = possible_positions[0][ind], possible_positions[1][ind]
+        room[player_position] = 5
+
+    for _ in range(num_boxes):
+        possible_positions = np.where(room == 1)
+        num_possible_positions = possible_positions[0].shape[0]
+
+        ind = np.random.randint(num_possible_positions)
+        box_position = possible_positions[0][ind], possible_positions[1][ind]
+        room[box_position] = 2
+
+    return room
+
+
+def add_random_player_movement(
+    room_state: np.ndarray,
+    room_structure: np.ndarray,
+    move_probability: float = 0.5,
+    continue_probability: float = 0.5,
+    max_steps: int = 3,
+):
+    if random.random() > move_probability:
+        return room_state
+
+    player_pos = np.where(room_state == 5)
+    player_pos = np.array([player_pos[0][0], player_pos[1][0]])
+
+    previous_positions = [tuple(player_pos)]
+    steps_taken = 0
+
+    while steps_taken < max_steps:
+        valid_moves = []
+        for action in range(4):
+            change = CHANGE_COORDINATES[action]
+            next_pos = player_pos + change
+
+            if room_state[next_pos[0], next_pos[1]] in [1, 2] and tuple(next_pos) not in previous_positions:
+                valid_moves.append((action, next_pos))
+
+        if not valid_moves:
+            break
+
+        _, next_pos = random.choice(valid_moves)
+
+        room_state[player_pos[0], player_pos[1]] = room_structure[player_pos[0], player_pos[1]]
+        room_state[next_pos[0], next_pos[1]] = 5
+
+        player_pos = next_pos
+        previous_positions.append(tuple(player_pos))
+
+        steps_taken += 1
+
+        if steps_taken >= max_steps or random.random() > continue_probability:
+            break
+
+    return room_state
+
+
+def reverse_playing(room_state: np.ndarray, room_structure: np.ndarray, search_depth: int = 100):
+    box_mapping = {}
+    box_locations = np.where(room_structure == 2)
+    num_boxes = len(box_locations[0])
+    for idx in range(num_boxes):
+        box = (box_locations[0][idx], box_locations[1][idx])
+        box_mapping[box] = box
+
+    explored_states: set[bytes] = set()
+    best_room_score = -1
+    best_room = room_state.copy()
+    best_box_mapping = box_mapping.copy()
+    best_action_sequence: List[int] = []
+
+    stack: deque = deque(
+        [
+            (
+                room_state.copy(),
+                box_mapping.copy(),
+                0,
+                (-1, -1),
+                search_depth,
+                [],
+            )
+        ]
+    )
+
+    while stack:
+        state, mapping, box_swaps, last_pull, ttl, action_sequence = stack.pop()
+        ttl -= 1
+        if ttl <= 0 or len(explored_states) >= 300000:
+            continue
+
+        state_hash = marshal.dumps(state)
+        if state_hash in explored_states:
+            continue
+
+        room_score = box_swaps * box_displacement_score(mapping)
+        if np.where(state == 2)[0].shape[0] != num_boxes:
+            room_score = 0
+
+        if room_score > best_room_score:
+            best_room = state.copy()
+            best_room_score = room_score
+            best_box_mapping = mapping.copy()
+            best_action_sequence = action_sequence.copy()
+
+        explored_states.add(state_hash)
+
+        for action in ACTION_LOOKUP_INTERNAL.keys():
+            if action >= 4:
+                continue
+
+            state_next = state.copy()
+            mapping_next = mapping.copy()
+
+            state_next, mapping_next, last_pull_next = reverse_move(
+                state_next, room_structure, mapping_next, last_pull, action
+            )
+
+            box_swaps_next = box_swaps
+            if last_pull_next != last_pull:
+                box_swaps_next += 1
+
+            action_sequence_next = action_sequence + [action]
+            stack.append(
+                (
+                    state_next,
+                    mapping_next,
+                    box_swaps_next,
+                    last_pull_next,
+                    ttl,
+                    action_sequence_next,
+                )
+            )
+
+    return best_room, best_box_mapping, best_action_sequence
+
+
+def reverse_move(
+    room_state: np.ndarray,
+    room_structure: np.ndarray,
+    box_mapping: Dict[Tuple[int, int], Tuple[int, int]],
+    last_pull: Tuple[int, int],
+    action: int,
+):
+    player_position = np.where(room_state == 5)
+    player_position = np.array([player_position[0][0], player_position[1][0]])
+
+    change = CHANGE_COORDINATES[action % 4]
+    next_position = player_position + change
+
+    if room_state[next_position[0], next_position[1]] in [1, 2]:
+        room_state[player_position[0], player_position[1]] = room_structure[player_position[0], player_position[1]]
+        room_state[next_position[0], next_position[1]] = 5
+
+        if action < 4:
+            possible_box_location = change[0] * -1, change[1] * -1
+            possible_box_location = (
+                possible_box_location[0] + player_position[0],
+                possible_box_location[1] + player_position[1],
+            )
+
+            if room_state[possible_box_location[0], possible_box_location[1]] in [3, 4]:
+                room_state[player_position[0], player_position[1]] = 3
+                room_state[possible_box_location[0], possible_box_location[1]] = room_structure[
+                    possible_box_location[0], possible_box_location[1]
+                ]
+
+                for key in list(box_mapping.keys()):
+                    if box_mapping[key] == (
+                        possible_box_location[0],
+                        possible_box_location[1],
+                    ):
+                        box_mapping[key] = (player_position[0], player_position[1])
+                        last_pull = key
+
+    return room_state, box_mapping, last_pull
+
+
+def box_displacement_score(box_mapping: Dict[Tuple[int, int], Tuple[int, int]]):
+    score = 0
+    for box_target, location in box_mapping.items():
+        box_location = np.array(location)
+        box_target_arr = np.array(box_target)
+        dist = np.sum(np.abs(box_location - box_target_arr))
+        score += dist
+    return score
diff --git a/resources_servers/grl_sokoban/sokoban_env/sokoban_env.py b/resources_servers/grl_sokoban/sokoban_env/sokoban_env.py
new file mode 100644
index 000000000..6d81f1934
--- /dev/null
+++ b/resources_servers/grl_sokoban/sokoban_env/sokoban_env.py
@@ -0,0 +1,117 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Local Sokoban environment implementation for the NeMo Gym server.
+
+This module adapts the environment used in the GRL repository while keeping all
+runtime dependencies inside the Gym project. It relies on ``gym_sokoban`` for
+core Sokoban mechanics and provides deterministic room generation utilities
+vendored into this directory.
+
+This implementation is based on code from https://github.com/lmgame-org/lmenv,
+developed in collaboration with NVIDIA.
+"""
+
+from __future__ import annotations
+
+import random
+from typing import Any, Dict
+
+import gymnasium as gym
+import numpy as np
+from gym_sokoban.envs.sokoban_env import SokobanEnv as GymSokobanEnv
+
+from .generation import generate_room
+
+
+class SokobanEnv(GymSokobanEnv):
+    """Self-contained Sokoban environment used by the resource server."""
+
+    def __init__(self, config: Dict[str, Any], **kwargs: Any) -> None:
+        self.config = config
+        self.GRID_LOOKUP = self.config.get("grid_lookup", {0: "#", 1: "_", 2: "O", 3: "√", 4: "X", 5: "P", 6: "S"})
+        self.ACTION_LOOKUP = self.config.get("action_lookup", {1: "Up", 2: "Down", 3: "Left", 4: "Right"})
+        self.search_depth = self.config.get("search_depth", 300)
+        self.ACTION_SPACE = gym.spaces.Discrete(4, start=1)
+        self.render_mode = self.config.get("render_mode", "text")
+
+        super().__init__(
+            dim_room=self.config.get("dim_room", (6, 6)),
+            max_steps=self.config.get("max_steps", 100),
+            num_boxes=self.config.get("num_boxes", 1),
+            **kwargs,
+        )
+
+    def reset(self, seed: int | None = None):  # type: ignore[override]
+        python_state = None
+        numpy_state = None
+        if seed is not None:
+            python_state = random.getstate()
+            numpy_state = np.random.get_state()
+            random.seed(seed)
+            np.random.seed(seed)
+
+        try:
+            (
+                self.room_fixed,
+                self.room_state,
+                self.box_mapping,
+                _action_sequence,
+            ) = generate_room(
+                dim=self.dim_room,
+                num_steps=self.num_gen_steps,
+                num_boxes=self.num_boxes,
+                search_depth=self.search_depth,
+            )
+        except (RuntimeError, RuntimeWarning):  # pragma: no cover - rare fallback
+            next_seed = abs(hash(str(seed))) % (2**32) if seed is not None else None
+            return self.reset(next_seed)
+        finally:
+            if seed is not None and python_state is not None and numpy_state is not None:
+                random.setstate(python_state)
+                np.random.set_state(numpy_state)
+
+        self.num_env_steps = 0
+        self.reward_last = 0
+        self.boxes_on_target = 0
+        self.player_position = np.argwhere(self.room_state == 5)[0]
+
+        return self.render()
+
+    def step(self, action: int):  # type: ignore[override]
+        previous_pos = self.player_position.copy()
+        _, reward, done, _ = super().step(action)
+        next_obs = self.render()
+        action_effective = not np.array_equal(previous_pos, self.player_position)
+        info = {
+            "action_is_effective": action_effective,
+            "action_is_valid": True,
+            "success": self.boxes_on_target == self.num_boxes,
+        }
+        return next_obs, reward, done, info
+
+    def render(self, mode: str | None = None):  # type: ignore[override]
+        render_mode = mode if mode is not None else self.render_mode
+        if render_mode == "text":
+            room = np.where((self.room_state == 5) & (self.room_fixed == 2), 6, self.room_state)
+            return "\n".join("".join(self.GRID_LOOKUP.get(int(cell), "?") for cell in row) for row in room.tolist())
+        if render_mode == "rgb_array":
+            return self.get_image(mode="rgb_array", scale=1)
+        raise ValueError(f"Invalid render mode: {render_mode}")
+
+    def get_all_actions(self):
+        return list(self.ACTION_LOOKUP.keys())
+
+    def close(self):  # type: ignore[override]
+        self.render_cache = None
+        super().close()
diff --git a/resources_servers/grl_sokoban/tests/test_app.py b/resources_servers/grl_sokoban/tests/test_app.py
new file mode 100644
index 000000000..0fdc1883a
--- /dev/null
+++ b/resources_servers/grl_sokoban/tests/test_app.py
@@ -0,0 +1,252 @@
+# Copyright (c) 2025, NVIDIA CORPORATION.  All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from unittest.mock import MagicMock, patch
+
+from fastapi.testclient import TestClient
+
+from nemo_gym.openai_utils import (
+    NeMoGymResponse,
+    NeMoGymResponseCreateParamsNonStreaming,
+)
+from nemo_gym.server_utils import ServerClient
+from resources_servers.grl_sokoban.app import (
+    GrlSokobanResourcesServer,
+    GrlSokobanResourcesServerConfig,
+)
+
+
+_VERIFY_CREATE_PARAMS = NeMoGymResponseCreateParamsNonStreaming(
+    input="placeholder",
+)
+
+_VERIFY_RESPONSE = NeMoGymResponse.model_construct(
+    id="resp_test",
+    object="response",
+    created_at=0.0,
+    status="completed",
+    output=[],
+    model="gpt-4.1",
+    parallel_tool_calls=True,
+    tool_choice="auto",
+    tools=[],
+)
+
+
+def _verify_payload() -> dict:
+    return {
+        "responses_create_params": _VERIFY_CREATE_PARAMS.model_dump(mode="json"),
+        "response": _VERIFY_RESPONSE.model_dump(mode="json"),
+    }
+
+
+class TestApp:
+    def test_sanity(self) -> None:
+        config = GrlSokobanResourcesServerConfig(
+            host="0.0.0.0",
+            port=8080,
+            entrypoint="",
+            name="",
+        )
+        GrlSokobanResourcesServer(config=config, server_client=MagicMock(spec=ServerClient))
+
+    def test_seed_and_step_flow(self) -> None:
+        config = GrlSokobanResourcesServerConfig(
+            host="0.0.0.0",
+            port=8080,
+            entrypoint="",
+            name="",
+        )
+        server = GrlSokobanResourcesServer(
+            config=config,
+            server_client=MagicMock(spec=ServerClient),
+        )
+
+        class FakeEnv:
+            ACTION_LOOKUP = {1: "Up"}
+
+            def __init__(self, *_args, **_kwargs) -> None:
+                self._closed = False
+                self.step_calls = 0
+
+            def reset(self, seed=None):  # noqa: ARG002
+                return "Initial observation"
+
+            def step(self, action):
+                self.step_calls += 1
+                assert action == 1
+                reward = 1.0
+                done = self.step_calls >= 1
+                info = {"success": done}
+                return "Next observation", reward, done, info
+
+            def close(self):
+                self._closed = True
+
+        fake_env = FakeEnv()
+        with patch("resources_servers.grl_sokoban.app.SokobanEnv", return_value=fake_env):
+            app = server.setup_webserver()
+            client = TestClient(app)
+
+            response = client.post("/seed_session", json={"seed": 123})
+            assert response.status_code == 200
+            assert response.json()["observation"] == "Initial observation"
+
+            cookies = response.cookies
+            response = client.post("/step", json={"actions": ["Up"]}, cookies=cookies)
+            payload = response.json()
+            assert response.status_code == 200
+            assert payload["observation"] == "Next observation"
+            assert payload["reward"] == 1.0
+            assert payload["done"] is True
+            assert payload["steps"][0]["action_label"] == "Up"
+            assert fake_env.step_calls == 1
+
+            response = client.post("/verify", json=_verify_payload(), cookies=cookies)
+            assert response.status_code == 200
+            payload = response.json()
+            assert payload["success"] is True
+            assert payload["reward"] == 1.0
+
+    def test_step_action_mapping_stops_after_done(self) -> None:
+        config = GrlSokobanResourcesServerConfig(
+            host="0.0.0.0",
+            port=8080,
+            entrypoint="",
+            name="",
+        )
+        server = GrlSokobanResourcesServer(
+            config=config,
+            server_client=MagicMock(spec=ServerClient),
+        )
+
+        class FakeEnv:
+            ACTION_LOOKUP = {1: "Up", 2: "Down"}
+
+            def __init__(self, *_args, **_kwargs) -> None:
+                self.calls = 0
+                self.closed = False
+
+            def reset(self, seed=None):  # noqa: ARG002
+                return "Init"
+
+            def step(self, action):
+                self.calls += 1
+                if self.calls == 1:
+                    assert action == 1
+                    return "Obs1", 0.5, True, {"success": True}
+                raise AssertionError("Env.step should not be called after done")
+
+            def close(self):
+                self.closed = True
+
+        fake_env = FakeEnv()
+        with patch("resources_servers.grl_sokoban.app.SokobanEnv", return_value=fake_env):
+            app = server.setup_webserver()
+            client = TestClient(app)
+
+            seed_resp = client.post("/seed_session", json={})
+            cookies = seed_resp.cookies
+            resp = client.post("/step", json={"actions": ["Up", "Down"]}, cookies=cookies)
+            payload = resp.json()
+            assert resp.status_code == 200
+            assert payload["done"] is True
+            assert payload["steps"][0]["action_label"] == "Up"
+            assert len(payload["steps"]) == 1
+            assert len(payload["history"]) == 1
+            assert fake_env.calls == 1
+
+    def test_step_invalid_action_raises(self) -> None:
+        config = GrlSokobanResourcesServerConfig(
+            host="0.0.0.0",
+            port=8080,
+            entrypoint="",
+            name="",
+        )
+        server = GrlSokobanResourcesServer(
+            config=config,
+            server_client=MagicMock(spec=ServerClient),
+        )
+
+        class FakeEnv:
+            ACTION_LOOKUP = {1: "Up"}
+
+            def __init__(self, *_args, **_kwargs) -> None:
+                pass
+
+            def reset(self, seed=None):  # noqa: ARG002
+                return "Init"
+
+            def step(self, action):  # pragma: no cover - not reached
+                raise AssertionError("Should not call step for invalid action")
+
+            def close(self):
+                pass
+
+        with patch("resources_servers.grl_sokoban.app.SokobanEnv", return_value=FakeEnv()):
+            app = server.setup_webserver()
+            client = TestClient(app)
+
+            seed_resp = client.post("/seed_session", json={})
+            cookies = seed_resp.cookies
+            resp = client.post("/step", json={"actions": ["Left"]}, cookies=cookies)
+            assert resp.status_code == 400
+            assert resp.json()["detail"].startswith("Unable to parse action")
+
+    def test_verify_failure_zero_reward_and_cleanup(self) -> None:
+        config = GrlSokobanResourcesServerConfig(
+            host="0.0.0.0",
+            port=8080,
+            entrypoint="",
+            name="",
+        )
+        server = GrlSokobanResourcesServer(
+            config=config,
+            server_client=MagicMock(spec=ServerClient),
+        )
+
+        class FakeEnv:
+            ACTION_LOOKUP = {1: "Up"}
+
+            def __init__(self, *_args, **_kwargs) -> None:
+                self.closed = False
+
+            def reset(self, seed=None):  # noqa: ARG002
+                return "Init"
+
+            def step(self, action):
+                return "Obs", 0.0, False, {"success": False}
+
+            def close(self):
+                self.closed = True
+
+        fake_env = FakeEnv()
+        with patch("resources_servers.grl_sokoban.app.SokobanEnv", return_value=fake_env):
+            app = server.setup_webserver()
+            client = TestClient(app)
+
+            seed_resp = client.post("/seed_session", json={})
+            cookies = seed_resp.cookies
+            client.post("/step", json={"actions": [1]}, cookies=cookies)
+
+            verify_resp = client.post(
+                "/verify",
+                json=_verify_payload(),
+                cookies=cookies,
+            )
+            assert verify_resp.status_code == 200
+            payload = verify_resp.json()
+            assert payload["success"] is False
+            assert payload["reward"] == 0.0
+            assert fake_env.closed is True
+            assert server.session_id_to_state == {}
diff --git a/responses_api_agents/simple_agent/app.py b/responses_api_agents/simple_agent/app.py
index 4fa6bb6b7..abd4ff993 100644
--- a/responses_api_agents/simple_agent/app.py
+++ b/responses_api_agents/simple_agent/app.py
@@ -44,6 +44,8 @@ class SimpleAgentConfig(BaseResponsesAPIAgentConfig):
     resources_server: ResourcesServerRef
     model_server: ModelServerRef
     max_steps: int = None
+    count_tool_calls: bool = False  # count tool calls instead of model calls toward steps
+    use_done_flag: bool = False  # stop when resource server returns {"done": true}
 
 
 class SimpleAgentRunRequest(BaseRunRequest):
@@ -74,6 +76,8 @@ async def responses(
 
         new_outputs = []
         step = 0
+        tool_call_count = 0
+        done_flag = False
         model_server_cookies = None  # update the cookies on every model response
         resources_server_cookies = request.cookies  # update the cookies on every resources server response
 
@@ -108,10 +112,9 @@ async def responses(
             all_output_messages: List[NeMoGymResponseOutputMessage] = [
                 o for o in output if o.type == "message" and o.role == "assistant"
             ]
-            if not all_fn_calls and all_output_messages:
-                break
 
             for output_function_call in all_fn_calls:
+                tool_call_count += 1
                 api_response = await self.server_client.post(
                     server_name=self.config.resources_server.name,
                     url_path=f"/{output_function_call.name}",
@@ -121,15 +124,31 @@ async def responses(
                 # We don't raise for status here since it's a valid return for the API to error e.g. if the model outputs an invalid call or something.
                 resources_server_cookies = api_response.cookies
 
+                raw_output = (await api_response.content.read()).decode()
+                if self.config.use_done_flag:
+                    try:
+                        parsed_output = json.loads(raw_output)
+                        if isinstance(parsed_output, dict):
+                            done_flag = done_flag or bool(parsed_output.get("done"))
+                    except json.JSONDecodeError:
+                        pass
+
                 tool_response = NeMoGymFunctionCallOutput(
                     type="function_call_output",
                     call_id=output_function_call.call_id,
-                    output=(await api_response.content.read()).decode(),
+                    output=raw_output,
                 )
                 new_outputs.append(tool_response)
 
+            if not all_fn_calls and all_output_messages:
+                break
+
             # Check if max steps is not None and if we have exhausted it.
-            if self.config.max_steps and step >= self.config.max_steps:
+            step_count = tool_call_count if self.config.count_tool_calls else step
+            if self.config.max_steps and step_count >= self.config.max_steps:
+                break
+
+            if self.config.use_done_flag and done_flag:
                 break
 
         # Propogate any extra cookies necessary for downstream verification