NVIDIA-NeMo · GPUtester · Sep 4, 2026 · Sep 4, 2026
@@ -212,7 +212,7 @@ SFT and LoRA both learn from examples of the right answer. Reinforcement learnin
 
 DPO trains on preference pairs. Each example is a prompt with a **chosen** response and a **rejected** one, and training pushes the model toward the chosen style without needing a separate reward model. Use it when you have human or model-generated preference data and want to shape tone, formatting, or helpfulness.
 
-The main control is `ref_policy_kl_penalty` (β), which sets how far the policy may drift from the model it started as.
+The main control is `ref_policy_kl_penalty` (β), which sets how far the policy may drift from the model it started as. See [DPO Customization](/documentation/customizer-reference/tutorials/dpo-customization-job).
 
 ### Group Relative Policy Optimization
 

@@ -373,7 +373,7 @@ A full-weight GRPO job instead registers a new model entity, which does need its
 
 ## Key hyperparameters
 
-All GRPO knobs live under `training`, except `policy_backend`, which is under `training.parallelism`.
+All GRPO knobs live under `training`, including `policy_backend` (a sibling of `parallelism`, not a field on it).
 
 | Field | Default | Notes |
 | --- | --- | --- |
@@ -384,7 +384,7 @@ All GRPO knobs live under `training`, except `policy_backend`, which is under `t
 | `overlong_filtering` | `false` | Zero the loss contribution of responses truncated at the generation cap. |
 | `ref_policy_kl_penalty` | `0.0` | KL coefficient against the reference policy. |
 | `val_at_start` | `false` | Run validation before the first step, so baseline and result come from one job. |
-| `parallelism.policy_backend` | `automodel` | The NeMo-RL worker that trains the model, chosen explicitly and never inferred. `automodel` supports LoRA, `expert_parallel_size` above 1, and `automodel_kwargs`, and needs Transformer Engine (Hopper or newer). `dtensor` runs stock HuggingFace modules on PyTorch FSDP2 for pre-Hopper GPUs, and supports none of those three. Requesting an `automodel`-only feature under `dtensor` is rejected at submit, with every conflict reported at once. |
+| `policy_backend` | `automodel` | The NeMo-RL worker that trains the model, chosen explicitly and never inferred. Set it on `training` (for example `training.policy_backend`), not under `training.parallelism`. `automodel` supports LoRA, `expert_parallel_size` above 1, and `automodel_kwargs`, and needs Transformer Engine (Hopper or newer). `dtensor` runs stock HuggingFace modules on PyTorch FSDP2 for pre-Hopper GPUs, and supports none of those three. Requesting an `automodel`-only feature under `dtensor` is rejected at submit, with every conflict reported at once. |
 | `batching_strategy` | `dynamic` | How rollouts are grouped into training micro-batches. `dynamic` fills each micro-batch to a token budget, so short rollouts share a batch instead of each paying for a full-length pad. `static` puts one rollout per slot. `sequence_packing` concatenates rollouts, and is rejected for VLM, multimodal, and context-parallel runs. |
 | `train_mb_tokens` | Derived | Token budget per micro-batch, read by `dynamic` and `sequence_packing`. Defaults to `max_seq_length × micro_batch_size`. |
 | `sequence_length_round` | `64` | Round bucketed sequence lengths up to a multiple of this. Read only by `dynamic`. |

@@ -133,6 +133,13 @@ Learn how to start a SFT customization job using a custom dataset.
 
 <small><span class="md-tag">nemo-customizer</span></small>
 
+</Card>
+<Card title="Start a DPO Customization Job" href="/documentation/customizer-reference/tutorials/dpo-customization-job">
+
+Learn how to start a DPO customization job using preference data.
+
+<small><span class="md-tag">nemo-customizer</span> <span class="md-tag">dpo</span></small>
+
 </Card>
 <Card title="Distill a Model with Knowledge Distillation" href="/documentation/customizer-reference/tutorials/distillation-customization-job">
 

@@ -54,7 +54,7 @@ print(f"Updated at: {cancelled_job.updated_at}")
   "name": "automodel-a1b2c3d4e5f6",
   "workspace": "default",
   "id": "platform-job-2k8i3i1HqJHHPVB5M6Bk9Z",
-  "source": "automodel",
+  "source": "customization",
   "status": "cancelled",
   "spec": {
     "model": "default/qwen3-1.7b",

@@ -143,6 +143,15 @@ print(f"Submitted job: {job.job.name}")
 Knowledge distillation is an Automodel feature. Set `training.training_type` to `"distillation"` and provide a `teacher_model` that references a second Model Entity. The `model` field is the student model being trained.
 
 ```python
+import os
+from nemo_platform import NeMoPlatform
+from nemo_automodel_plugin.schema import AutomodelJobInput
+
+client = NeMoPlatform(
+    base_url=os.environ.get("NMP_BASE_URL", "http://localhost:8080"),
+    workspace="default",
+)
+
 spec = AutomodelJobInput(
     model="default/qwen3-1.7b",  # Student model
     dataset={"training": "default/my-training-dataset"},

@@ -6,7 +6,7 @@ title: "List Active Jobs"
 description: ""
 ---
 <a id="ft-list-active-customization-jobs"></a>
-List active customization jobs and their high-level status. Customization jobs run on the platform's Jobs service, so you list them through that service and filter by `source` and `status`. The `source` scopes results to a backend (`automodel` or `unsloth`), while `status="active"` excludes completed, failed, and cancelled jobs. Each entry includes the job definition (model, dataset, training configuration) and overall status.
+List active customization jobs and their high-level status. Customization jobs run on the platform's Jobs service, so you list them through that service and filter by `source` and `status`. Platform job records use `source="customization"` for Automodel, Unsloth, and RL (the Jobs service does not store the training backend as `source`). `status="active"` excludes completed, failed, and cancelled jobs. Tell backends apart from the job name prefix (`automodel-…`, `unsloth-…`, `rl-…`) or from the backend you submitted to. Each entry includes the job definition (model, dataset, training configuration) and overall status.
 
 <Tip>
 
@@ -28,7 +28,7 @@ export NMP_BASE_URL="https://your-nmp-base-url"
 
 ## To List Active Customization Jobs
 
-Use the SDK to list jobs, filtering by `source` to scope the results to a customization backend and by `status` to return only active jobs:
+Use the SDK to list jobs, filtering by `source="customization"` and by `status` to return only active customization jobs:
 
 ```python
 import os
@@ -40,11 +40,11 @@ client = NeMoPlatform(
     workspace="default",
 )
 
-# List active automodel customization jobs
+# List active customization jobs
 jobs = client.jobs.list(
     workspace="default",
     filter={
-        "source": "automodel",  # Use "unsloth" for the Unsloth backend
+        "source": "customization",
         "status": "active",
     },
     page=1,
@@ -61,7 +61,7 @@ for job in jobs.data:
 filtered_jobs = client.jobs.list(
     workspace="default",
     filter={
-        "source": "automodel",
+        "source": "customization",
         "status": "active",
         "project": "my-finetuning-project",
     },
@@ -84,7 +84,7 @@ for job in filtered_jobs.data:
       "id": "platform-job-QtyhRY5ub4t4tTLPY4sTkz",
       "name": "automodel-99da3f7c1b2e",
       "workspace": "default",
-      "source": "automodel",
+      "source": "customization",
       "created_at": "2026-02-09T22:12:45",
       "updated_at": "2026-02-09T22:12:45",
       "status": "active",
@@ -126,7 +126,7 @@ for job in filtered_jobs.data:
   },
   "sort": "created_at",
   "filter": {
-    "source": "automodel",
+    "source": "customization",
     "status": "active"
   },
   "search": {}

@@ -175,31 +175,4 @@ Each line in your JSONL file must contain a JSON object with these required fiel
 }
 ```
 
-### SFT Legacy Conversational
-
-#### Required Schema
-
-Each line in your JSONL file must contain a JSON object with these required fields:
-
-- **`system`** (string): The system message that defines the assistant's role or behavior.
-- **`conversations`** (array of objects): The conversation turns between user and assistant.
-  - **`from`** (string): The role of the message sender (`User` or `Assistant`).
-  - **`value`** (string): The content of the message.
-
-#### Example Dataset Entry
-
-```json
-{
-  "system": "You are a helpful assistant.",
-  "conversations": [
-    {
-      "from": "User",
-      "value": "Choose a number that is greater than 0 and less than 2."
-    },
-    {
-      "from": "Assistant",
-      "value": "1"
-    }
-  ]
-}
-```
+The `system` plus `conversations: [{from, value}]` layout is **not** a supported SFT schema. Automodel detects chat data from a `messages` array (`role` / `content`), prompt/completion pairs, embedding triplets, or a custom two-column `prompt_template`. Convert conversational examples to [OpenAI Chat Model](#openai-chat-model) format before training; otherwise the job fails at start with `DatasetFormatError`.