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README.md

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 license: mit
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+
+# 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}
+}
+```