| --- |
| license: mit |
| --- |
| |
| # eVTA₀ Models |
|
|
| This repository hosts the released checkpoints of the paper |
| ["Demonstration-Free Success-Probability Reward Learning for Generalist Robot |
| Policies"](https://arxiv.org/abs/2609.33653). |
|
|
| - **Reward models** (`reward_model/`): the eVTA₀ success-probability reward |
| model trained on LIBERO and MetaWorld rollouts (`checkpoint_adapter.pth`). |
| The checkpoints contain only the LoRA adapter, the reward-token embedding, |
| and the prediction head — the backbone is not included; download |
| [Qwen/Qwen3-VL-4B-Instruct](https://huggingface.co/Qwen/Qwen3-VL-4B-Instruct) |
| separately and load the checkpoint on top of it. These checkpoints were |
| serialized under transformers 5.8.0 / peft 0.19.1; loading them requires |
| `transformers >= 5.8.0` and `peft >= 0.19.1`. |
| - **RL policies** (`pi05_policy/`): pi0.5 policies trained with GRPO driven by |
| the eVTA₀ reward, one per LIBERO suite |
| (`evta0-grpo-6400ep-{suite}-pi05.pt`). |
|
|
|
|
| **Code:** https://github.com/duowuyms/eVTA0 |
|
|
| **Paper:** https://arxiv.org/abs/2609.33653 |
|
|
| **Author:** [Duo Wu](https://duowuyms.github.io/) |
|
|
| ## Citation |
|
|
| If you find these models useful, please cite our paper: |
|
|
| ```bibtex |
| @article{wu2026evta0, |
| title={Demonstration-Free Success-Probability Reward Learning for Generalist Robot Policies}, |
| author={Wu, Duo and Wang, Haifeng and Lu, Rongwei and Wang, Jinghe and Xiong, Tianyi and Wang, Zhimin and Yu, Chao and Ma, Shuai and Wang, Zhi}, |
| journal={arXiv preprint arXiv:2609.33653}, |
| year={2026} |
| } |
| ``` |
|
|
