# Cosmos3 DROID Action-Policy Post-Training This example post-trains [Cosmos3-Nano](https://huggingface.co/nvidia/Cosmos3-Nano) into a DROID action policy on the [Cosmos3-DROID](https://huggingface.co/datasets/nvidia/Cosmos3-DROID) dataset, reproducing [Cosmos3-Nano-Policy-DROID](https://huggingface.co/nvidia/Cosmos3-Nano-Policy-DROID). The model predicts absolute joint-position actions conditioned on proprioceptive state and video observations at a resolution of 480p (`640×360`). Experiment `action_policy_droid_nano`, launcher `launch_sft_action_policy_droid_nano.sh`. Two external inputs are required: (1) a pre-downloaded Cosmos3-DROID dataset in LeRobotDataset v3.0 format, and (2) a DCP base checkpoint converted from the chosen base model (Cosmos3-Nano or Cosmos3-Edge). The recipe runs multi-node via HSDP (single node / 8 GPUs and beyond). ## Prerequisites - [Setup](https://github.com/NVIDIA/cosmos-framework/blob/main/README.md#setup) — clone the repo, install the training extras (`uv sync --all-extras --group=cu130-train`), and activate the environment. - [Environment Variables](environment_variables.md) — set environment variables. - [FAQ](faq.md) — troubleshooting (OOM during SFT, defaults) and common pitfalls. The runnable artifacts (TOML recipe, paired launch shell) live in [`examples/`](https://github.com/NVIDIA/cosmos-framework/tree/main/examples); all commands below run from the repo root with the environment activated. ## Inputs You Provide This package ships the training stack — the registered `action_policy_droid_nano` experiment, the DROID action dataset class with the recipe knobs (`action_space=joint_pos`, `use_state`, `concat_view`), and the EMA warm-start in `checkpoint/dcp.py`. Two inputs are external and must be provided per environment: 1. **[Cosmos3-DROID](https://huggingface.co/datasets/nvidia/Cosmos3-DROID) dataset (in LeRobotDataset v3.0 format)** — pre-download the dataset into a directory named `droid_plus_lerobot_640x360_20260412` (the loader resolves the dataset schema from the directory name) and point `DROID_ROOT` at that directory — the parent containing `success/`, not `.../success` itself. 2. **DCP base checkpoint** — convert [Cosmos3-Nano](https://huggingface.co/nvidia/Cosmos3-Nano) to DCP and point `BASE_CHECKPOINT_PATH` at it (see [Full Reproduction](#full-reproduction)). Action heads are not loaded from it (they init fresh). ## Recipe | knob | value | | ----------------- | -------------------------------------------------------------------------------------------------------------------------------------------------- | | init | `Cosmos3-Nano` (public Hugging Face repo) | | action space | `joint_pos` (absolute joint position, 8-D incl. gripper) | | state | `use_state=true` (proprioception; valid only with `joint_pos`) | | task mode | `policy` (single-task; the `joint` multi-task default is avoided) | | resolution | `480` | | viewpoint / video | `concat_view` / `video_mode=null` | | chunk length | `32` (tokenizer `encode_exact_durations=[33]`) | | sequence packing | `max_num_tokens_after_packing=-1` (full vision sequence per step) | | shuffle | episode-shuffle stream (decorrelates the per-step global batch) | | window filter | [keep_ranges_1_0_1.json](https://huggingface.co/KarlP/droid/blob/main/keep_ranges_1_0_1.json) (`KarlP/droid`) — trains the curated ≈74% window set | | lr | `2e-4` | | global batch | `8192` = `max_samples_per_batch` × world size × `grad_accum_iter` (reduce the first / raise the last to fit GPU memory) | | eval | disabled for the reproduction run | ## Full Reproduction The OSS flow mirrors the other recipes (see [docs/training.md](training.md)): ```shell # Step 1: download Cosmos3-DROID into a directory named droid_plus_lerobot_640x360_20260412 # (the loader resolves the dataset schema from the directory name), then point # DATASET_PATH at that parent directory — the one containing success/, not .../success. hf download nvidia/Cosmos3-DROID --repo-type dataset \ --local-dir /path/to/droid_plus_lerobot_640x360_20260412 export DATASET_PATH=/path/to/droid_plus_lerobot_640x360_20260412 # Step 2: convert the base checkpoint -> $BASE_CHECKPOINT_PATH python -m cosmos_framework.scripts.convert_model_to_dcp \ --checkpoint-path Cosmos3-Nano \ -o $BASE_CHECKPOINT_PATH # Step 3: download the keep_ranges_1_0_1.json window filter (drops idle/non-task frames -> trains # the curated ~74% window set, matching the released model). export FILTER_DIR=/path/to/droid_filters hf download KarlP/droid keep_ranges_1_0_1.json --local-dir $FILTER_DIR # Step 4: launch. The TOML selects the experiment + scalars; the dataset/action # knobs come from the registered experiment. export BASE_CHECKPOINT_PATH=/path/to/base_checkpoint export WAN_VAE_PATH=/path/to/Wan2.2_VAE.pth export NPROC_PER_NODE=8 # Enable the keep_ranges_1_0_1.json filter via EXTRA_TAIL_OVERRIDES (space-separated Hydra # overrides; an exported string survives `bash `). $FILTER_DIR is expanded HERE, # at export time — it must already be set in this shell, or filter_dict_path silently # becomes /keep_ranges_1_0_1.json and the dataset fails with FileNotFoundError. : "${FILTER_DIR:?set FILTER_DIR (Step 3) before composing EXTRA_TAIL_OVERRIDES}" export EXTRA_TAIL_OVERRIDES=" \ dataloader_train.dataloader.datasets.droid.dataset.use_filter_dict=True \ dataloader_train.dataloader.datasets.droid.dataset.filter_dict_path=$FILTER_DIR/keep_ranges_1_0_1.json \ " bash examples/launch_sft_action_policy_droid_nano.sh ``` The recipe TOML ([`action_policy_droid_nano.toml`](https://github.com/NVIDIA/cosmos-framework/blob/main/examples/toml/sft_config/action_policy_droid_nano.toml)) sets the scalar knobs (`max_iter`, `save_iter`, `grad_clip`, parallelism, wandb); the dataset/action knobs (`joint_pos`, `use_state`, `concat_view`, 480p, chunk 32, count-based batch) live in the registered `action_policy_droid_nano` experiment per the schema's design. For multi-node HSDP, set `model.config.parallelism.data_parallel_replicate_degree = ` (intra-node shard stays 8). The **keep_ranges_1_0_1.json filter** maps each DROID trajectory key to a list of `[start, end]` frame ranges; only windows whose start falls inside a kept range are trained on (episodes absent from the dict are dropped). To train on the full window set instead, leave `EXTRA_TAIL_OVERRIDES` unset. ## Checkpoints - Saved every `save_iter` iters (1000 in the validated run) to the object store, at `////checkpoints/iter_/`. - The run is **resumable** from the latest checkpoint (re-launch with the same `job.name`). - Export to HF safetensors via `cosmos_framework.scripts.export_model` (see [docs/training.md](training.md)).