# InternW0 RoboTwin-clean checkpoint provenance

## Selected artifact

`InternW0-RoboTwin-clean.pt` is the model-only checkpoint selected for the
RoboTwin 2.0 Clean2Random release. It is derived from the historical run
`w0-cln-cj02-s2-64g-dh-0919r5` at step 10,545.

The historical source implementation was `ahawam`; the public runtime is `w0`.
The resolved numerical training contract is preserved in
`source_training_config.yaml` with private filesystem paths replaced by portable
environment variables.

## Lineage

1. Heterogeneous InternW0 pretraining at global step 30,000.
2. RoboTwin target-domain initialization with `new_domain` action/proprio
   interfaces, seed 42.
3. Clean-demonstration posttraining run
   `w0-cln-p30k-cj02-s2-64g-0918r3`, resumed at step 5,000.
4. Continued best Clean2Random recipe
   `w0-cln-cj02-s2-64g-dh-0919r5`, selected at step 10,545.

The checkpoint embeds `behavior_config`, `training_recipe`,
`posttrain_statistics`, and `posttrain_provenance`. These fields are also
exported verbatim to `checkpoint_metadata.json`.

## Training contract

- Clean demonstrations only.
- RoboTwin native joint-space representation.
- 25 Hz sampling (`sampling_stride=2` from 50 Hz).
- Every valid native window.
- ColorJitter: brightness 0.2, contrast 0.2, saturation 0.2, hue 0.
- Offset 0.
- Batch size 12 per process.
- 15 epochs.
- Learning rate 3e-5, cosine schedule.
- Seed 42.
- 37-dimensional canonical action/proprio interface.
- 14 valid ALOHA joint/gripper slots:
  `[0, 1, 2, 3, 4, 5, 16, 17, 18, 19, 20, 21, 22, 33]`.
- Action horizon 64, chunk size 16, video plan horizon 96, video/action stride 8.
- K=8 target soft prompts.
- Native absolute joint commands at the interval end.

The matching normalization file is `normalization.json`.

## Evaluation reference

The source resolved evaluation reference is preserved in
`source_evaluation_config.yaml`. It uses the fixed-period RoboTwin adapter at
25 Hz, 10 inference steps, a 64-action horizon, four action chunks per video
prefill, and 15 episodes per task.

The paper-reported RoboTwin 2.0 Clean2Random results are:

| Split | Success rate |
|---|---:|
| Clean | 83.20% |
| Randomized (unseen during training) | 68.00% |
| Average | 75.60% |

## Excluded from this repository

The checkpoint package does not include optimizer state, distributed training
state, logs, RoboTwin data, Wan2.2 files, DINOv3 weights, text caches, or
simulation checkouts.