TimeRewarder: MetaWorld checkpoints

Paper: https://arxiv.org/abs/2509.26627
Project page: https://timerewarder.github.io

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README.md

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+---
+license: mit
+tags:
+- reinforcement-learning
+- reward-model
+- robotics
+- metaworld
+- learning-from-videos
+---
+
+# TimeRewarder — MetaWorld checkpoints
+
+Pretrained **TimeRewarder** checkpoints for 10 MetaWorld tasks, from the paper:
+
+**[TimeRewarder: Learning Dense Reward from Passive Videos via Frame-wise Temporal Distance](https://arxiv.org/abs/2509.26627)**
+Yuyang Liu\*, Chuan Wen\*, Yihang Hu, Dinesh Jayaraman, Yang Gao†
+
+[🌐 Project Page](https://timerewarder.github.io/) · [📄 Paper](https://arxiv.org/abs/2509.26627) · [💻 Code](https://github.com/CowAndSheep/TimeRewader) · [🤗 Demos](https://huggingface.co/datasets/CowAndSheep/timerewarder-demos)
+
+TimeRewarder learns a dense reward from passive (action-free) videos by predicting the frame-wise temporal distance between frames; the per-frame progress prediction is used directly as the reward for downstream RL.
+
+## Contents
+
+One vision-only checkpoint per task (`<task>_20bins.pth`, 20 discrete progress bins):
+`basketball`, `button-press-topdown`, `disassemble`, `door-open`, `drawer-open`, `lever-pull`, `plate-slide`, `stick-push`, `window-close`, `window-open`.
+
+```bash
+huggingface-cli download CowAndSheep/timerewarder --local-dir models/ckpt
+```
+
+Drop the `.pth` files into `models/ckpt/` and run the downstream RL as described in the [code repository](https://github.com/CowAndSheep/TimeRewader).
+
+## Citation
+
+```bibtex
+@article{liu2025timerewarder,
+  title={TimeRewarder: Learning Dense Reward from Passive Videos via Frame-wise Temporal Distance},
+  author={Liu, Yuyang and Wen, Chuan and Hu, Yihang and Jayaraman, Dinesh and Gao, Yang},
+  journal={arXiv preprint arXiv:2509.26627},
+  year={2025}
+}
+```

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