README.md

---
task_categories:
- robotics
tags:
- reinforcement-learning
- robomimic
---

# From Prior to Pro: Efficient Skill Mastery via Distribution Contractive RL Finetuning (DICE-RL)

[**Project Website**](https://zhanyisun.github.io/dice.rl.2026/) | [**Paper**](https://huggingface.co/papers/2603.10263) | [**GitHub**](https://github.com/zhanyisun/dice-rl)

This repository contains the datasets used in **DICE-RL**, a framework that uses reinforcement learning as a "distribution contraction" operator to refine pretrained generative robot policies. The data includes both pretraining data (for Behavior Cloning) and finetuning data (for DICE-RL) across various Robomimic environments.

## Dataset Structure

The datasets are provided in `numpy` format, and each folder typically contains `train.npy` and `normalization.npz`. The data is organized following this structure:

```
data_dir/
└── robomimic
    ├── {env_name}-low-dim
    │   ├── ph_pretrain
    │   └── ph_finetune
    └── {env_name}-img
        ├── ph_pretrain
        └── ph_finetune
```

- **ph_pretrain**: Contains the datasets used for pretraining the BC policies.
- **ph_finetune**: Contains the datasets used for finetuning the DICE-RL policies. These are similar to the pretraining sets but with trajectories truncated to ensure value learning consistency between offline and online data (truncated to have exactly one success at the end).
- **low-dim**: State-based observations.
- **img**: High-dimensional pixel (image) observations.

## Usage

You can download the datasets using the scripts provided in the [GitHub repository](https://github.com/zhanyisun/dice-rl):

```console
bash script/download_hf.sh
```

For more details on generating your own data or processing raw Robomimic datasets, please refer to the project's [dataset processing guide](https://github.com/zhanyisun/dice-rl/blob/main/script/dataset/README.md).

## Citation

```bibtex
@article{sun2026prior,
  title={From Prior to Pro: Efficient Skill Mastery via Distribution Contractive RL Finetuning},
  author={Sun, Zhanyi and Song, Shuran},
  journal={arXiv preprint arXiv:2603.10263},
  year={2026}
}
```