README.md

---
license: cc-by-4.0
datasets:
- teetone/RoboReward
language:
- en
base_model:
- Qwen/Qwen3-VL-8B-Instruct
---


# RoboReward 8B

**Paper:** [https://arxiv.org/abs/2601.00675](https://arxiv.org/abs/2601.00675)

RoboReward provides **general-purpose vision-language reward model for robotics**, trained on the [RoboReward dataset](https://huggingface.co/datasets/teetone/RoboReward) with **Qwen-3 VL** to predict **discrete end-of-episode progress rewards** from real-robot rollout videos.  


## Usage

### Purpose

Given a **task instruction** and a **rollout video**, the model predicts an end-of-episode progress score:
- **1:** No success  
- **2:** Minimal progress  
- **3:** Partial completion  
- **4:** Near completion  
- **5:** Perfect completion  

### Inference

Follow the [original Qwen 3-VL instructions with video input](https://huggingface.co/Qwen/Qwen3-VL-8B-Instruct) and use a text prompt like this:

```text
Given the task, assign a discrete progress score reward (1,2,3,4,5) for the robot in the video in the format: ANSWER: <score>
Rubric for end-of-episode progress (judge only the final state without time limits):
1 - No Success: Final state shows no goal-relevant change for the command.
2 - Minimal Progress: Final state shows a small but insufficient change toward the goal.
3 - Partial Completion: The final state shows good progress toward the goal but violates more than one requirement or a major requirement.
4 - Near Completion: Final state is correct in region and intent but misses a single minor requirement.
5 - Perfect Completion: Final state satisfies all requirements.

Task: <INSERT TASK HERE>
```

## Citation

```bibtex
@misc{lee2026roborewardgeneralpurposevisionlanguagereward,
      title={RoboReward: General-Purpose Vision-Language Reward Models for Robotics}, 
      author={Tony Lee and Andrew Wagenmaker and Karl Pertsch and Percy Liang and Sergey Levine and Chelsea Finn},
      year={2026},
      eprint={2601.00675},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2601.00675}, 
}
```