cosmos3-post-training git:20260917.99b7bd7A

Immutable. This exact content never changes and is served at /api/v1/blob/8c90d8238d2a849c.

---
name: cosmos3-post-training
description: Use when planning, reviewing, or explaining Cosmos3 supervised fine-tuning and post-training in NPA, including upstream recipes, dataset/checkpoint preparation, and why NPA does not expose post-training as a fake skill command.
---

# Cosmos3 Post-Training

## Source And Attribution

Adapted from NVIDIA cosmos-framework
`skills/workflows/cosmos3-post-training/SKILL.md`.

Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. Used under OpenMDW-1.1.
See `skills/LICENSE-NVIDIA-COSMOS3-OPENMDW-1.1` and
`skills/NOTICE-NVIDIA-COSMOS3`.

## When To Use

Use this skill when the user asks how Cosmos3 SFT works, how to review future
post-training support, where upstream recipes live, how to validate training
configs, or whether an NPA change should expose post-training.

For current NPA, treat Cosmos3 post-training as guidance and planning unless a
real executable workflow is implemented and tested. Do not add a Cosmos
skill-display subcommand or a SkyPilot YAML whose only purpose is to make this
agent skill runnable.

## Current NPA Boundary

The experimental `workflows/testing/cosmos3-policy-model-factory.yaml` now
invokes native LIBERO-10 SFT through `npa workbench cosmos3 policy-train`, then
the matching `policy-eval`, `policy-feedback`, and `failure-candidates`
primitives. See `docs/workbench/cosmos3-policy-model-factory.md` and its linked
readiness record before claiming GPU qualification. The new shared modules are
`npa/src/npa/workbench/cosmos/policy_*.py`; CLI and SDK call these implementations.

The runtime synchronizes a separate pinned `cu130-train` environment. Do not
treat the baked inference environment as training support. Keep the native
20 Hz LIBERO action, camera, normalization, and gripper semantics paired with
the simulator. A reduced execution smoke cannot qualify the policy: selection
requires all ten tasks and fifty trials each. Absolute qualification does not
prove improvement against an incumbent. Generated videos remain ineligible for
action training until labels are independently validated. Cross-run training
resume and automatic subsequent learning rounds are not implemented.

Retained real Cosmos3 workflows:

- `workflows/testing/cosmos-fetch.yaml`
- `workflows/testing/cosmos3-text-to-image.yaml`

Current NPA Cosmos commands such as `npa workbench cosmos train` cover the
existing Cosmos workbench/serverless training surface, not a proven Cosmos3 SFT
workflow. Do not present that as Cosmos3 post-training unless implementation and
tests explicitly support it.

## Upstream Post-Training Map

In a clone of `https://github.com/NVIDIA/cosmos-framework.git`, inspect:

| Need | Upstream path |
| --- | --- |
| Training guide | `docs/training.md` |
| Dataset JSONL/captioning guide | `docs/dataset_jsonl.md` |
| SFT recipes | `examples/toml/sft_config/<recipe>.toml` |
| Paired recipe launchers | `examples/launch_sft_<recipe>.sh` |
| Common launcher helper | `examples/_sft_launcher_common.sh` |
| Training script | `cosmos_framework/scripts/train.py` |
| DCP conversion | `cosmos_framework/scripts/convert_model_to_dcp.py` |
| HF export | `cosmos_framework/scripts/export_model.py` |
| TOML schema | `cosmos_framework/configs/toml_config/sft_config.py` |

## Planning Checklist

When reviewing or designing NPA Cosmos3 post-training support:

1. Define the exact executable outcome: config validation, dry run, training,
   checkpoint conversion, export, or inference from a trained checkpoint.
2. Require explicit dataset, base checkpoint, and Wan VAE paths where the
   upstream recipe requires them.
3. Keep training extras explicit: `cu130-train` or `cu128-train`.
4. Validate TOML/schema behavior with upstream `train.py --dryrun` before
   claiming training support.
5. Use temporary or user-selected output roots, not repository paths.
6. Preserve redaction for Hugging Face, GitHub, NGC, S3, and any other secret
   env values.
7. Add tests that prove NPA maps inputs into a real executable workflow. Do not
   use tests that only prove an agent skill can be listed or displayed by a CLI.

## Upstream Workflow At A Glance

The upstream flow is:

1. Install training extras and clear `LD_LIBRARY_PATH` if needed.
2. Prepare the dataset and any required VAE artifact.
3. Convert the base Hugging Face checkpoint to DCP when the recipe requires it.
4. Launch the paired `examples/launch_sft_<recipe>.sh` script or an equivalent
   raw `torchrun` command.
5. Find checkpoints under the upstream training output root.
6. Run inference with the trained DCP checkpoint and its `config.yaml`.
7. Optionally export to Hugging Face safetensors.

If the user needs inference after training, switch to
`skills/workflows/cosmos3-inference/SKILL.md` and point `--checkpoint-path`
plus any upstream config file at the trained checkpoint output.