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Merge main to async_rl (feat. checkpoint engine for fully_async) - #2

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What does this PR do?

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chengminhua and others added 30 commits December 2, 2025 14:33
…process in the NPU. (verl-project#4209)

### What does this PR do?

Fix issues that occur during the ACLGraph initialization process in the
NPU.
***verl/workers/megatron_workers.py*** : This is to fix the issue of not
entering the graph after ACLGraph is enabled.
***verl/utils/vllm/patch.py***: This is to fix the issue where the MoE
model encounters an error when loading weights after enabling ACLGraph.

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---------

Co-authored-by: Changsheng Quan (全昌盛) <quancs@qq.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
for now (latest TE=2.10), gptoss's optimized attn kernel is not
supported for thd format, so we use bshd format here.
when bshd format is used, we need to pad the input_ids to the longest
sequence length
so we recommend to disable dynamic batch size and set micro batch size
to 1 to avoid paddings
but it is ok to try with micro_batch_size>1

see `test_dapo_gptoss_20b_megatron.sh` for example.

<img width="1299" height="867" alt="image"
src="https://github.com/user-attachments/assets/b166a4b7-9c3a-4840-84c1-e8de02b506db"
/>

The training crashes with mismatch, need further experiments with
MIS/TIS or fp16
### What does this PR do?

> Fix the bug that causes asynchronous checkpoint saving to fail when
using the Megatron backend.

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---------

Signed-off-by: 李鸣沼 <lmztju@126.com>
Co-authored-by: 李鸣沼 <limingzhao3@h-partners.com>
### What does this PR do?

- As title

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…project#4379)

### What does this PR do?

This PR fixes a crash that occurs when serializing a `DataProto` object
with an empty `TensorDict` batch.




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…t#4378)

### What does this PR do?

This PR adds the `full_determinism` option to the model engine to
improve training reproducibility. Note: This option defaults to false,
as this negatively impacts performance, so it's only for debugging
purposes.

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### What does this PR do?

- Support ray-based sft trainer
- Add tests
- Add tests for tensordict concat with only NonTensorStack and
NonTensorData
- Modify CheckpointHandler to support both SPMD mode and ray mode

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---------

Co-authored-by: Joel <wuxibin89@163.com>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
…roject#4386)

adopt mbridge's fast saving API
https://github.com/ISEEKYAN/mbridge/blob/main/mbridge/core/bridge.py#L302-L303,
which will reduce the saving time of large models a lot.

In my test, the deepseek 671B saving time is under 2 minutes
… loop (verl-project#4358)

### What does this PR do?

Reward computation in reward loop will follow the design below:

```
Reward Computation Logic:
- if user-customized reward function is provided:
    -> directly use user-customized reward function
- if user-customized reward function is not provided:
    -> rm is not enabled: use default rule-based reward function
    -> rm is disrm: compute reward score using disrm
    -> rm is genrm: raise error (user-costomized reward func must be provided)
```

This PR adds support for DisRM and polishes some code for GenRM and
Rule-Based reward, with corresponding ci tests.

The legacy FSDP/Megatron reward models can soon be deprecated, as all
scenarios are supported (i.e., rule, disrm, genrm).

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…ject#4307)

### What does this PR do?

> Add **concise** overview of what this PR aims to achieve or
accomplish. Reference related GitHub issues and PRs that help with the
review.

we refactor one step off to support server mode.
1. only support server mode
2. change  old log probs to rollout log probs
3. alse support agent loop

Under 128 cards, there is approximately a 2.09x return with no reduction
in effectiveness.

Detailed test results can be obtained via the link below:

https://wandb.ai/hou-zg-meituan/one-step-off-128?nw=nwuserhouzg

<img width="1064" height="718" alt="image"
src="https://github.com/user-attachments/assets/18766c69-19c0-4b9e-a7c7-86b709c12d70"
/>
<img width="1010" height="608" alt="image"
src="https://github.com/user-attachments/assets/2efbab0b-9c0e-41e1-9402-d54e476208ac"
/>
<img width="1010" height="608" alt="image"
src="https://github.com/user-attachments/assets/0663fa59-a203-438c-8505-9f05a4ae2651"
/>

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### What does this PR do?

- Support TensorDict in DataProtoFuture
- Add tests
- Support Nonblocking train and infer in TrainingClient, and ray-based
sft trainer

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…n + remove_padding scenario (verl-project#4165)

fix the BUG caused by empty tensors in the multi_turn + remove_padding
scenario

### What does this PR do?

> Add **concise** overview of what this PR aims to achieve or
accomplish. Reference related GitHub issues and PRs that help with the
review.

In multi-turn scenarios, the attention_mask may encounter full masking.
When use_remove_padding is enabled and mbs is sufficiently small, the
forward input_ids and position_ids may become an empty tensor,
triggering an error.

This PR creates an input containing at least one element during full
masking and removes it from the output, preventing this error and
enhancing code robustness.

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I used qwen 3b with mbs set to 1, and compared the reward between
enabling use_remove_padding (the branch will follow the modified code)
and not enabling it.
<img width="661" height="734" alt="image"
src="https://github.com/user-attachments/assets/6ca1f501-7216-46e7-80e3-a6ecfef96dd2"
/>

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…rl-project#4359)

## Summary

This PR adds new rollout correction estimators and improves
documentation consistency:

1. **New Estimators**: Add `geo_rs_seq_tis()` and `pg_geo_rs_seq_tis()`
preset methods - the "ultimate" estimators for reasoning models and
agents
2. **Terminology**: Add consistent terminology (Seq-TIS, Seq-MIS,
Geo-RS, Geo-RS-Seq-TIS, Length Trap)
3. **Documentation Reorganization**: Reorganize preset tables by
operating mode (Decoupled PPO / Bypass PPO / Bypass PG)
4. **URL Update**: Replace Notion URL with shorter blog URL
`https://richardli.xyz/rl-collapse`
5. **Title Fix**: Fix reference title to "Training-Inference Mismatch"
(was incorrectly "Inference-Training" in some places)
6. **BibTeX Citation**: Update citation format in documentation

## Files Changed

| File | Changes |
|------|---------|
| `docs/algo/rollout_corr.md` | Add new presets, reorganize tables,
update URL, add BibTeX |
| `docs/algo/rollout_corr_math.md` | Add estimator hierarchy,
combination matrix, update URL, add BibTeX |
| `verl/trainer/config/algorithm.py` | Add `geo_rs_seq_tis()` and
`pg_geo_rs_seq_tis()` methods, update docstrings |
| `verl/trainer/ppo/rollout_corr_helper.py` | Update URL and reference
title |
| `recipe/dapo/run_dapo_qwen2.5_32b_rollout_corr.sh` | Update URL and
reference title |

## New Preset Methods

| Preset Method | Estimator | Mode | Description |
|---------------|-----------|------|-------------|
| `geo_rs_seq_tis()` | Geo-RS-Seq-TIS | Decoupled PPO | Geometric filter
+ clipped seq IS weight |
| `pg_geo_rs_seq_tis()` | Geo-RS-Seq-TIS | Bypass PG | Same estimator in
bypass mode (faster) |

## Verification

- [x] All 10 preset methods documented and implemented
- [x] All URLs point to `https://richardli.xyz/rl-collapse`
- [x] All titles use consistent "Training-Inference Mismatch" wording
- [x] Tables organized by operating mode (Decoupled PPO / Bypass PPO /
Bypass PG)
- [x] No functional code changes (documentation and presets only)

## Reference

- Blog: [When Speed Kills Stability: Demystifying RL Collapse from the
Training-Inference Mismatch](https://richardli.xyz/rl-collapse)
### What does this PR do?

This PR supports manually specifying `master_addr` and `master_port` for
`RayWorkerGroup`, which can be useful for avoiding collisions when there
are multiple `RayWorkerGroup`s in a single node. Note this modification
does not change the default behavior and has full backward
compatibility.

### Test

See the CI.
### What does this PR do?

as title

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### What does this PR do?

This PR introduces a draft **Router Replay** support into Verl.
Inspired by the recent research in **MoE Reinforcement
Learning**([2510.11370](https://arxiv.org/abs/2510.11370),
[2507.18071](https://arxiv.org/abs/2507.18071)), this implementation
supports **Router Replay (R2)** and **Rollout Router Replay (R3)**.
R2 allows recording routing token selection during` log probability
computation` and replaying expert selection during policy update. R3
enables recording during `model inference` and replaying during RL
post-training.

The initial version supports **Router Replay** with `Megatron` backend,
including comprehensive support for distributed training strategies
(**DP, TP, EP, ETP, PP, and Re-compute**).


The current implementation uses a patch-based approach. Once the
upstream PR
[NVIDIA/Megatron-LM#2101](NVIDIA/Megatron-LM#2101)
is merged or provides corresponding interfaces, the patch can be removed
and replaced with official API integration.

## Usage Tutorial

### Basic Configuration
To enable Router Replay functionality, add the following configuration
to your trainer config:
#### Method 1: Trainer Configuration
Add the following configuration to your trainer config:

```yaml
router_replay:
  enabled: true
  mode: "R2"  # Options: "R2", "R3"
```

#### Method 2: Launch Script Configuration
Add the following parameter to your launch script:

```bash
# In your launch script
actor_rollout_ref.actor.router_replay.mode="R2"
```

### R2 Mode Usage
1. **Enable R2 mode** in configuration
2. **Record phase**: During log probability computation, routing
selections are automatically recorded
3. **Replay phase**: During policy update, recorded expert selections
are replayed

### R3 Mode Usage
1. **Enable R3 mode** in configuration
2. **Record phase**: During model inference, routing decisions are
captured
3. **Replay phase**: During RL post-training, recorded routing data is
used
4. 
## In Progress
R2
- [ ]  FSDP backend

R3
- [x] vLLM Rollout
- [ ] Sglang Rollout

---------

Co-authored-by: litianjian <litianjian@bytedance.com>
Co-authored-by: zhangbiao.168 <zhangbiao.168@bytedance.com>
## Summary

This PR refactors the FlowRL actor implementation by removing
CISPO-specific features and simplifying to a pure FlowRL trajectory
balance objective
  with importance weight clipping.

  ## Changes

  ### Removed
- **Ablation study code**: Deleted `compute_flowrl_cispo_clip_ablation`
function and environment variable switching logic


  ### Modified
- **Function rename**: `compute_flowrl_cispo_clip` → `compute_flowrl` to
better reflect the pure implementation
- **Simplified masking**: Now uses `response_mask` directly without
additional condition-based filtering
- **Cleaner metrics**: Keeps essential metrics (log_prob, log_z,
importance_weight, PPO KL, reference KL)

  ### Kept
- **Core FlowRL objective**: Trajectory balance loss `L = E[w * (log Z +
log p_θ - β*R - log p_ref)²]`
- **Importance weight clipping**: Maintains stability with `max=10`
clipping
- **Log partition function (log Z)**: Projection network for estimating
partition function

---------

Co-authored-by: Claude <noreply@anthropic.com>
…ct#4409)

### What does this PR do?

update reward loop doc

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### What does this PR do?

This PR mainly:

1. supports specifying custom `reward_manager` with
`reward_manager.module.path` as the path to the module file containing
the reward manager definition (e.g.,
`/path/to/custom/reward_manager.py`) and `reward_manager.name` as the
class name (e.g., `CustomRewardManager`),
2. deprecates `reward_model.reward_manager` to introduce
`reward_manager` config, while keeping backward compatibility,

and by the way,

3. deprecates `load_extern_type` to introduce more general `load_module`
and `load_extern_object`,
4. moves some unnecessary imports under `TYPE_CHECKING` using
`__future__.annotations`.

### Test

See the CI.

### API and Usage Example

Previous:

```python
reward_model:
     reward_manager: dapo
```

Now:

```python
reward_manager:
    source: register
    name: dapo
```

or

```python
reward_manager:
    source: importlib
    module:
        path: /path/to/module.py
        name: custom_reward_manager
    name: CustomRewardManager
```
### What does this PR do?

Retires the legacy SPMD rollout path and standardizes the codebase on
async-only rollout for vLLM (SGLang in the next PR). All Python modules,
docs, workflows, and examples now reference the async server mode
exclusively; the sync/SPMD runners, helpers, and CI jobs have been
removed.

### Checklist Before Starting

- [ ] Search for similar PRs. Paste at least one query link here: _N/A
(internal task to delete SPMD support)._
- [ ] Format the PR title as `[vllm, sglang, rollout, trainer, recipe,
ci, doc] refactor: remove SPMD rollout`

### Test

Not run (SPMD suites deleted; async flow already covered by existing
CI).

### API and Usage Example

All configs/scripts must now use `actor_rollout_ref.rollout.mode=async`.
Example:

```bash
python -m verl.trainer.main_ppo \
  ... \
  actor_rollout_ref.rollout.name=vllm \
  actor_rollout_ref.rollout.mode=async \
  ...
```

### Design & Code Changes

- Deleted `verl/workers/rollout/vllm_rollout/vllm_rollout_spmd.py` and
the entire SGLang SPMD engine, leaving only async implementations.
Updated `BaseRollout` registry, `RolloutConfig`, and `main_ppo` to error
on `mode=sync`.
- Removed SPMD-specific docs, tests
(`tests/workers/rollout/test_sglang_*`, `test_vllm_spmd`,
`test_vllm_model_rope_scaling`), and CI steps
(`.github/workflows/vllm.yml`, `sgl.yml`). Simplified lint exclusions
and helper scripts accordingly.
- Cleaned recipes/examples to default `rollout_mode=async` and
eliminated conditional sync branches (`examples/**`, `recipe/**`, e2e
scripts). Added explicit validation in agent-loop utilities and SFT
runner to reject non-async requests.
- Updated documentation (FS- DP/Megatron worker guides, hybrid flow,
r1_ascend notes, FP8 guide) to describe async-only rollout and mention
removal of the old SPMD pathway.

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SPMD jobs; async coverage already exists._
- [ ] Once your PR is ready for CI, notify the `ci-request` channel (or
Feishu group).
…ntegration (verl-project#4408)

### What does this PR do?

> Resolve the issue that Prometheus and Grafana cannot monitor relevant
metrics during rollout using SGLang.

### Problems

When using SGLang as the rollout engine, the HTTP server fails to start
properly. As a result, Prometheus cannot scrape metrics and Grafana is
unable to display them.

### Details

Set the **warmup_thread_args** property: Configure
app.warmup_thread_args before starting the server to satisfy SGLang’s
lifespan requirements.

Skip server warmup: Enable **skip_server_warmup=True** to avoid
connection issues caused by the warmup thread. Since the model is
already loaded in _launch_subprocesses, no additional warmup is
required.

Add **Prometheus middleware** early: Manually call
add_prometheus_middleware before invoking run_unvicorn to ensure that
the /metrics endpoint is available immediately when the server starts.

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<img width="595" height="246" alt="截屏2025-12-04 下午3 00 59"
src="https://github.com/user-attachments/assets/3aed1690-172c-4857-b880-5af5371646bf"
/>
<img width="595" height="246" alt="截屏2025-12-04 下午3 01 05"
src="https://github.com/user-attachments/assets/517077d7-7c96-4ac9-a2c5-0f06ea6701b3"
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---------

Co-authored-by: jsfanfanfan <2981866535@qq.com>
Co-authored-by: AniZpZ <aniz1905@gmail.com>
…erl-project#4420)

### What does this PR do?

As title.

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…#4422)

### What does this PR do?

Retires the legacy SGLang SPMD rollout path and makes async/server mode
the only supported backend for SGLang. The PR removes the old
`SGLangRollout` class, its helpers, tests, and recipes, and updates all
docs, scripts, and CI references so they speak only to the async HTTP
adapter (`ServerAdapter`).

### Checklist Before Starting

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(follow-up to the vLLM SPMD removal)._
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doc] refactor: remove SGLang SPMD rollout`

### Test

### API and Usage Example

Same as vLLM: configs/scripts must set
`actor_rollout_ref.rollout.mode=async` and rely on the HTTP server.
Example:

```bash
python -m verl.trainer.main_ppo \
  ... \
  actor_rollout_ref.rollout.name=sglang \
  actor_rollout_ref.rollout.mode=async \
  ...
```

### Design & Code Changes

- Deleted the `SGLangRollout` class and associated helpers from
`verl/workers/rollout/sglang_rollout/sglang_rollout.py`, keeping only
the async `ServerAdapter`. Cleared its registry entries, configs, and
guards the same way as the vLLM PR.
- Removed SGLang SPMD-specific tests
(`tests/workers/rollout/test_sglang_*`) and CI steps in
`.github/workflows/sgl.yml`, plus any lint exclusions that referenced
those files.
- Updated recipes/examples/e2e scripts that referenced SGLang rollout to
hardcode `rollout.mode=async`, drop sync branches, and set
`return_raw_chat` (mirroring the vLLM cleanup).

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…ct#4406)

### What does this PR do?

This PR optimizes Qwen3-MoE model training performance on Ascend NPU
devices. It optimizes the implementation of **GMM (Grouped Matmul)** and
integrates fused **permute/unpermute** kernels, achieving a 20%+
training speedup.

Key changes:
1. Added NPU GMM kernel for backward `dw`.
2. Added `npu_moe_token_permute` and `npu_moe_token_unpermute` fused
kernels.
3. Unified GMM function for Qwen3-VL and Qwen3-MoE.
4. Reduced transpose operators in expert weight stacking.

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Tested with Qwen3-30B-A3B (FSDP, sp=8) on 64 Ascend A2 NPUs.
Baseline:

![img_v3_02sj_639d375c-ef1e-4e3f-9c3e-afa1c6696feg](https://github.com/user-attachments/assets/87615508-13c1-4cdf-96c0-e1ee09fa99d1)
With optimized fusion kernels: 

![img_v3_02sj_401d607a-5bb0-42d8-bf07-af47a06cfd4g](https://github.com/user-attachments/assets/6a2f2ca7-e741-416b-b892-234b75b0f809)
<!DOCTYPE html><p cid="n349" mdtype="paragraph" class="md-end-block
md-p" style="box-sizing: border-box; line-height: inherit; orphans: 4;
margin: 0.8em 0px; white-space: pre-wrap; position: relative;
caret-color: rgb(51, 51, 51); color: rgb(51, 51, 51); font-family:
&quot;Open Sans&quot;, &quot;Clear Sans&quot;, &quot;Helvetica
Neue&quot;, Helvetica, Arial, &quot;Segoe UI Emoji&quot;, sans-serif;
font-size: 16px; font-style: normal; font-variant-caps: normal;
font-weight: 400; letter-spacing: normal; text-align: start;
text-indent: 0px; text-transform: none; word-spacing: 0px;
-webkit-text-stroke-width: 0px; background-color: rgb(255, 255, 255);
text-decoration: none;"><span md-inline="strong" class="md-pair-s"
style="box-sizing: border-box;"><strong style="box-sizing:
border-box;"><span md-inline="plain" class="md-plain" style="box-sizing:
border-box;">Performance results (step 1)</span></strong></span><span
md-inline="plain" class="md-plain" style="box-sizing:
border-box;">:</span></p><figure class="md-table-fig" cid="n350"
mdtype="table" style="box-sizing: border-box; margin: 1.2em 0px;
overflow-x: auto; max-width: calc(100% + 16px); padding: 0px; cursor:
default; caret-color: rgb(51, 51, 51); color: rgb(51, 51, 51);
font-family: &quot;Open Sans&quot;, &quot;Clear Sans&quot;,
&quot;Helvetica Neue&quot;, Helvetica, Arial, &quot;Segoe UI
Emoji&quot;, sans-serif; font-size: 16px; font-style: normal;
font-variant-caps: normal; font-weight: 400; letter-spacing: normal;
orphans: auto; text-align: start; text-indent: 0px; text-transform:
none; white-space: normal; widows: auto; word-spacing: 0px;
-webkit-text-stroke-width: 0px; background-color: rgb(255, 255, 255);
text-decoration: none;">
Experiment | gen(s) | old_log_prob(s) | update_actor(s) | step(s)
-- | -- | -- | -- | --
Baseline | 1180.1 | 71.3 | 152.5 | 1406.9
Fused (This PR) | 1167.5 | 58.6 | 111.1 | 1340.9

</figure>

**Precision comparison:**
<img width="664" height="360" alt="image"
src="https://github.com/user-attachments/assets/5e6629c4-31b5-49ec-97cf-d5e4e6beb69c"
/>

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### What does this PR do?

As per title.

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### What does this PR do?

> Add **concise** overview of what this PR aims to achieve or
accomplish. Reference related GitHub issues and PRs that help with the
review.

With an increasing number of models on verl being adapted to Ascend
NPUs, along with the upgrade of the transformers version (currently
`v4.57.3`), the content in the `npu_patch.py` has grown significantly.
Its organization has gradually become cluttered, lacking a unified
naming format, and a small portion of the patches have already become
obsolete (the original functions no longer exist).

This PR aims to address the above issues, ensuring that `npu_patch.py`
remains clean and well-organized.

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…-project#4430)

### What does this PR do?

Prevents background event-loop leaks in the MCP tool loader.
`initialize_tools_from_config` now lazily spawns the auxiliary asyncio
loop only when an MCP tool is present, and always stops/closes the loop
on exit so the worker process doesn’t hold onto threads or loop
resources.

### Checklist Before Starting

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(targeted fix for the MCP tool registry)._
- [ ] Format the PR title as `[tool, misc] fix: clean up MCP tool event
loop`

### Test

Not applicable (behavioral fix in tool initialization; existing
tool-based tests still cover the code path).

### API and Usage Example

No API surface changes; the existing YAML config flow stays the same.
Example usage:

```python
from verl.tools.utils.tool_registry import initialize_tools_from_config

tool_instances = initialize_tools_from_config("configs/tools.yaml")
```

### Design & Code Changes

- Replaced the eager `asyncio.new_event_loop()` creation with a lazy
`get_mcp_event_loop()` helper so purely native tool configs no longer
spawn threads.
- Simplified coroutine execution to always go through the
lazily-initialized loop.
- Added robust cleanup in the `finally` block: stop the loop, join the
thread, and call `loop.close()` to release resources (fixing the leak).

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…icy distillation recipe (verl-project#3975)

### What does this PR do?

This PR provides a simple implementation of one and two step off async 
knowledge distillation with megatron and vllm backend.

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---------

Co-authored-by: funrunding <furunding@163.com>
study8677 and others added 29 commits December 11, 2025 13:15
…ect#4486)

### What does this PR do?

> This PR corrects a spelling error in the docstring of the
`setup_embeddings_and_output_layer` method within
`verl/models/qwen2/megatron/modeling_qwen2_megatron.py`. Specifically,
it changes `initalizes` to `initializes`.

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  - Title: `[megatron] fix: correct typo in modeling_qwen2_megatron.py`

### Test

> This is a trivial documentation fix (docstring only) and does not
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> - Visual inspection of the change.

### API and Usage Example

> N/A - This change only affects internal code documentation.

### Design & Code Changes

> **Modifications:**
> - `verl/models/qwen2/megatron/modeling_qwen2_megatron.py`: Fixed typo
`initalizes` -> `initializes`.

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… 4.57.x (verl-project#4402)

fix qwen3vlmoe with Monkey patch to fix a bug in transformers 4.57.3
where `Qwen3VLMoeTextSparseMoeBlock.forward`

incorrectly uses torch.zeros_like(router_logits) instead of
torch.zeros_like(hidden_states)
    when creating router_weights (line 148 in modeling_qwen3_vl_moe.py).

to close issue: verl-project#4401 RuntimeError: scatter(): Expected self.dtype to be
equal to src.dtype
### What does this PR do?

> Add **concise** overview of what this PR aims to achieve or
accomplish. Reference related GitHub issues and PRs that help with the
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---------

Co-authored-by: qian.cheng <mgr.qiancheng@gmail.com>
Co-authored-by: wuxibin <wuxibin@bytedance.com>
### What does this PR do?

As per title.

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…ject#4489)

### What does this PR do?

> Add **concise** overview of what this PR aims to achieve or
accomplish. Reference related GitHub issues and PRs that help with the
review.

For Ascend NPU, **maintaining consistency between the currently
developed test cases and the GPU test cases in the verl community is
crucial for ongoing maintenance.**

However, the scripts for NPU and GPU differ in their handling of the
trainer.device parameter. **Heavy reliance on this parameter forces the
NPU side to separately archive corresponding test scripts, making it
impossible to directly reuse existing GPU scripts or future additions.**

To address this, this PR plans to **automatically set device_name to
`npu` on Ascend NPU devices.**

Following entrances are equiped with this mechenism:
 - verl.trainer.main_ppo
 - verl.trainer.fsdp_sft_trainer
 - recipe.dapo.main_dapo
 - recipe.transfer_queue.main_ppo
 - recipe.one_step_off_policy.main_ppo
 - recipe.r1_ascend.main_ppo

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### What does this PR do?

- Make reward loop disrm default, user can specify
`reward_model.use_reward_loop=True` to enable and "False" to disable
(True as default).
- Architecture design as follows:

<img width="910" height="631" alt="image"
src="https://github.com/user-attachments/assets/767c7413-2b52-4759-99b1-a44c0bcf8989"
/>

I have also tested the precision and find the gap between legacy fsdp
disrm and reward loop disrm acceptable, with results as follows (legacy
disrm in orange and reward loop disrm in red):

<img width="784" height="634" alt="image"
src="https://github.com/user-attachments/assets/68d60ec3-0ba1-4a2c-9f66-97e0b490c9da"
/>


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## Summary

- Refactor rollout correction: replace `use_policy_gradient: bool` with
`loss_type: str`
- Rename presets with consistent `bypass_` prefix
- Register loss function as `@register_policy_loss("bypass_mode")`

## Changes

### Config Parameter
| Before | After |
|--------|-------|
| `use_policy_gradient: bool = False` | `loss_type: str = "ppo_clip"` |

`loss_type` accepts:
- `"ppo_clip"` (default): PPO clipped objective (IS handled by ratio)
- `"reinforce"`: REINFORCE with explicit IS weights

### Preset Method Renames
| Before | After |
|--------|-------|
| `ppo_is_bypass()` | `bypass_ppo_clip()` |
| `pg_is()` | `bypass_pg_is()` |
| `pg_rs()` | `bypass_pg_rs()` |
| `pg_geo_rs_seq_tis()` | `bypass_pg_geo_rs_seq_tis()` |
| - | `bypass_ppo_clip_geo_rs()` (new) |

### Loss Function Renames
| Before | After |
|--------|-------|
| `compute_policy_loss_with_rollout_correction()` |
`compute_policy_loss_reinforce()` |
| `compute_policy_loss_rollout_correction_wrapper()` |
`compute_policy_loss_bypass_mode()` |
| `@register_policy_loss("rollout_correction")` |
`@register_policy_loss("bypass_mode")` |

### Implementation
- `compute_policy_loss_bypass_mode()` dispatches to REINFORCE or
PPO-clip based on `loss_type`
- PPO-clip: ratio handles IS, no explicit IS weights (avoids
double-counting)
- REINFORCE: explicit IS weights applied as multiplicative factor
- Both modes support rejection sampling (RS)

## Test plan

- [ ] Verify `RolloutCorrectionConfig.bypass_ppo_clip()`,
`.bypass_pg_is()`, etc.
- [ ] Test bypass mode with `loss_type="ppo_clip"` (default)
- [ ] Test bypass mode with `loss_type="reinforce"`
- [ ] Verify `@register_policy_loss("bypass_mode")` dispatch
- [ ] Run existing rollout correction tests
### What does this PR do?

Due to fused worker design and ActorRolloutRef is a async Ray Actor, we
have to temporarily implement `train_mini_batch` API for TrainingWorker
and use it to implement update_actor and update_critic
In this PR, we
- Completely remove CriticWorker in engine and directly use
TrainingWorker
- Implement e2e ppo process using model engine
- Results on char count task matches with legacy worker implementation
- Note that currently megatron backend for model engine value model is
not supported until mbridge officially supports value model save/load

<img width="365" height="267" alt="image"
src="https://github.com/user-attachments/assets/4edafe71-6929-4a3a-84fa-d653cad826e0"
/>

- orange: ppo with legacy worker
- blue: ppo with model engine worker
- white: grpo with model engine worker



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---------

Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
…oject#4519)

### What does this PR do?

Add a reset option after calling the abort request in vLLM.
Default reset prefix cache after abort.
If not reset, it may become pipelinerl.

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…ager (verl-project#4505)

### What does this PR do?

This PR fixes the load balancing issue in AsyncLLMServerManager where
the heap-based server selection was using hash values instead of
indices, causing unpredictable server selection order after shuffling.

Problem:

The original implementation used hash(server) as the secondary sort key
in the heap
When all servers had the same request count (0), the heap would select
the server with the minimum hash value, not the first server in the
shuffled list
This resulted in poor load distribution and defeated the purpose of
random shuffling

Solution:

Replace hash(server) with explicit indices in the heap structure
Heap now sorts by (request_count, index, server) instead of
(request_count, hash, server)
Ensures deterministic selection: when request counts are equal, the
server with the lowest index (first in the shuffled list) is always
chosen

Example: 

# Before (❌ Broken):
server_handles = [Server_A, Server_B, Server_C]
random.shuffle(server_handles)  # → [Server_C, Server_A, Server_B]
weighted_servers = [[0, hash(s), s] for s in server_handles]
# Heap might select Server_A first (min hash), not Server_C!

# After (✅ Fixed):
server_handles = [Server_A, Server_B, Server_C]
random.shuffle(server_handles)  # → [Server_C, Server_A, Server_B]
weighted_servers = [[0, idx, s] for idx, s in enumerate(server_handles)]
# Heap correctly selects Server_C first (idx=0)

Co-authored-by: Aleksandr Semikin <aesemikin@alice-a100.sas.yp-c.yandex.net>
…age improvement (verl-project#4429)

### What does this PR do?

1. Support zero-copy serialization for SimpleUnit backend through
environment variable `TQ_ZERO_COPY_SERIALIZATION`.
TransferQueue/TransferQueue#121
2. When calling `tq_client.async_put()`, TQ will return the updated
`BatchMeta` object to the user. This will improve user experience when
using TQ. TransferQueue/TransferQueue#119
3. Add log for transferqueue_utils.py
4. Add new BatchMeta operations to simplify single-controller usage


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---------

Signed-off-by: 0oshowero0 <o0shower0o@outlook.com>
…roject#4533)

### What does this PR do?

Now that several important fixes have been merged into Megatron-Bridge,
it's better to update the instructions so that everything can really
work correctly.

Related to:
- NVIDIA-NeMo/Megatron-Bridge#1564
- NVIDIA-NeMo/Megatron-Bridge#1603
- NVIDIA-NeMo/Megatron-Bridge#1627
- NVIDIA-NeMo/Megatron-Bridge#1628

Fix: verl-project#4303

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<sub>✨ Presented to you with <a href="https://macaron.im/mindlab">Mind
Lab</a> - A Lab for Experiential Intelligence.</sub>

Signed-off-by: Hollow Man <hollowman@opensuse.org>
…dge by default (verl-project#4528)

### What does this PR do?

this is one of a series PRs to clean the legacy megatron code path and
make bridge the default path for megatron. verl-project#4496

This PR make megatron engine use only support bridge, and mark the
legacy code in `mcore/registry.py`

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…l-project#4529)

### What does this PR do?

this is one of a series PRs to clean the legacy megatron code path and
make bridge the default path for megatron.
verl-project#4496

This PR make clean CIs, including qwen2.5vl megatron, model merger,
config converter


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…erl-project#4492)

### What does this PR do?

Fix RL model engine for VLM. 

Qwen/Qwen3-VL-30B-A3B-Instruct fsdp vs megatron on geo3k:
<img width="386" height="310" alt="image"
src="https://github.com/user-attachments/assets/f04e38b7-514a-4792-9806-3ad7964aa797"
/>
…erl-project#4543)

### What does this PR do?

> Add **concise** overview of what this PR aims to achieve or
accomplish. Reference related GitHub issues and PRs that help with the
review.

`Ray==2.46.0` discover some security vulnerabilities, including
unauthorized default states and arbitrary code execution, as follows:
 - GHSA-gx77-xgc2-4888
 - GHSA-q279-jhrf-cc6v

This PR is committed for removing ray version restriction for
`requirements-npu.txt`.

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### What does this PR do?

- Migrate all Reward-Model-related CI to Reward Loop (verified)
- Set the naive router as the default for the reward loop

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## Summary

Refactor `agg_loss` function and fix entropy/KL loss scaling in
distributed training.

**Changes:**
- **Refactor**: Unify `seq-mean-*` modes with shared denominator logic
using `masked_sum`
- **Behavior change**: `seq-mean-token-sum-norm` now applies seq-mean
division (denominator = `global_batch_size * dp_size` or `local_bsz`),
matching the mode name
- **Simplification**: Remove fully-masked sequence exclusion from
denominator; use total batch size consistently

NOTE: Since the global loss aggregation logic is not compatible with the
legacy model engine that conduct the aggregation outside `agg_loss` and
is going to be deprecated, we keep this PR from modifying the the legacy
model engine.

⚠️ **Breaking**: `seq-mean-token-sum-norm` now divides by both
`loss_scale_factor` AND `seq_denominator`. Previously only divided by
`loss_scale_factor`.

## Test plan

- [ ] Verify PPO training with `seq-mean-token-sum` mode
- [ ] Verify PPO training with `seq-mean-token-mean` mode  
- [ ] Verify PPO training with `seq-mean-token-sum-norm` mode (note:
behavior changed)
- [ ] Confirm entropy/KL loss values are correctly scaled in multi-GPU
training

---------

Co-authored-by: Shawn/Yuxuan Tong <tongyuxuan361@gmail.com>
…ect#4549)

### What does this PR do?

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this was missed in the rebase
verl-project@0102d04

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Co-authored-by: Ryan Li <rynli@amazon.com>
…ject#4521)

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- 在fully-async 模式下,添加enable_sleep_mode 参数支持,可以使用虚拟显存功能,可充分利用显存,减少OOM 现象
- 默认下是True,与原来保持一致

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可参考[recipe/fully_async_policy/shell/dapo_30b_a3b_base_math_fsdp.sh]
脚本进行使用,新增`+actor_rollout_ref.rollout.enable_sleep_mode=False\` 命令即可;

### API and Usage Example

> Demonstrate how the API changes if any, and provide usage example(s)
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# Add code snippet or script demonstrating how to use this
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> Demonstrate the high-level design if this PR is complex, and list the
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…verl-project#4548)

### What does this PR do?

This PR enhances the extensibility of the agent loop module by:

1. **Supporting custom AgentLoopManager**: Allow users to specify a
custom `AgentLoopManager` class via configuration
(`agent_loop_manager_class`), enabling external projects to implement
their own rollout management logic.

2. **Extracting gpt-oss tool response builder**: Extract the gpt-oss
specific tool response formatting logic into a reusable utility function
`build_gpt_oss_tool_response_text()`, combining existing
`format_gpt_oss_tool_response_manually()` and
`add_generation_prompt_for_gpt_oss()` calls.

3. **Adding extension point for custom configurations**: Introduce a
`custom: Optional[dict]` field in `RolloutConfig` to support arbitrary
user-defined configurations.

### Checklist Before Starting

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### Test

This PR is a refactoring and extension of existing functionality:

- The `build_gpt_oss_tool_response_text()` function maintains the same
behavior as the original inline code in `ToolAgentLoop`
- The custom `AgentLoopManager` loading uses `importlib` which is a
standard Python mechanism
- No behavioral changes to existing workflows when
`agent_loop_manager_class` is not configured

### API and Usage Example

#### 1. Custom AgentLoopManager

Users can now specify a custom `AgentLoopManager` class in their config:

```yaml
actor_rollout_ref:
  rollout:
    mode: async
    agent:
      agent_loop_manager_class: "myproject.custom_manager.MyAgentLoopManager"
```

> **Security Note**: The `agent_loop_manager_class` configuration uses
dynamic import via `importlib`, which will execute the specified
module's code. Only use class paths from trusted sources. Ensure your
configuration files are not writable by untrusted users or processes.

#### 2. Reusable gpt-oss Tool Response Builder

External projects can now import and use the gpt-oss utility:

```python
from verl.experimental.agent_loop.utils import build_gpt_oss_tool_response_text

# Build gpt-oss tool response text (combines formatting + generation prompt)
tool_response_text = build_gpt_oss_tool_response_text(messages, tool_call_names)
```

#### 3. Custom Configuration Extension Point

```yaml
actor_rollout_ref:
  rollout:
    custom:
      my_custom_key: "my_custom_value"
      another_setting: 123
```

### Design & Code Changes

#### High-Level Design

```
┌─────────────────────────────────────────────────────────────────┐
│                      ray_trainer.py                             │
│  ┌───────────────────────────────────────────────────────────┐  │
│  │  if agent_loop_manager_class configured:                  │  │
│  │      dynamically import via importlib                     │  │
│  │  else:                                                    │  │
│  │      use default AgentLoopManager                         │  │
│  └───────────────────────────────────────────────────────────┘  │
└─────────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────────┐
│         verl/experimental/agent_loop/utils.py (new export)      │
│  ┌───────────────────────────────────────────────────────────┐  │
│  │  build_gpt_oss_tool_response_text()                       │  │
│  │    - combines format_gpt_oss_tool_response_manually()     │  │
│  │    - and add_generation_prompt_for_gpt_oss()              │  │
│  └───────────────────────────────────────────────────────────┘  │
└─────────────────────────────────────────────────────────────────┘
```

#### Specific Changes

| File | Changes |
|------|---------|
| `verl/trainer/ppo/ray_trainer.py` | Added dynamic loading of custom
`AgentLoopManager` via `importlib` |
| `verl/workers/config/rollout.py` | Added `agent_loop_manager_class` to
`AgentLoopConfig`; Added `custom` field to `RolloutConfig` |
| `verl/experimental/agent_loop/utils.py` | Added
`build_gpt_oss_tool_response_text()` combining existing gpt-oss
formatting functions |
| `verl/experimental/agent_loop/tool_agent_loop.py` | Refactored to use
`build_gpt_oss_tool_response_text()` for cleaner code |

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### What does this PR do?

Previous PRs about reward loop may introduce some different names, which
may cause foncusion to developers. I plan to rename them to clearer
folder/file/class names:

For file/folder name:

- `verl/experimental/reward` -> `verl/experimental/reward_loop` (this
PR)
- `verl/experimental/reward_loop/reward_manager` ->
`verl/experimental/reward_loop/reward_loop.py` (same as naming logic of
agent_loop)
- `verl/experimental/reward/reward_loop` ->
`verl/experimental/reward/reward_manager` (the new name is the same as
the legacy reward manager, and they actually do the same thing; the new
reward manager will deprecate the legacy in the future)

For class name:

- The reward manager class name will be kept the same as the old ones,
like `DAPORewardManager`, ...

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### What does this PR do?

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As title, add third-party download cache into e2e_ascend additionally.

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As title.

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…ct#4552)

### What does this PR do?

- As title

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### What does this PR do?

as title

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…ngine. only for fully_async mode. (verl-project#4427)

### What does this PR do?

supporting efficient parameter sync between trainer and rollouter for
fully_async mode.

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@ZSL98
ZSL98 merged commit aa5f14c into async_rl Dec 18, 2025
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