README.md

---
base_model:
- Qwen/Qwen2.5-VL-7B-Instruct
license: apache-2.0
metrics:
- mae
- accuracy
pipeline_tag: video-text-to-text
---

# PRIMO R1: Process Reasoning Induced Monitoring

This repository contains the model weights for PRIMO R1, introduced in the paper [From Passive Observer to Active Critic: Reinforcement Learning Elicits Process Reasoning for Robotic Manipulation](https://huggingface.co/papers/2603.15600).

## Model Description

PRIMO R1 is a 7B framework designed to transform video Multimodal Large Language Models (MLLMs) from passive "Observers" into active "Critics" for long-horizon robotic manipulation. While traditional models often focus on recognizing ongoing events, PRIMO R1 evaluates the current state of a task relative to its final goal.

The model is fine-tuned from [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) using outcome-based Reinforcement Learning to elicit explicit Chain-of-Thought (CoT) generation for progress estimation. Its architecture incorporates a structured temporal input that anchors video sequences between the initial and current state images.

## Key Features

- **RL-Induced Reasoning**: Uses outcome-based RL to incentivize the generation of thought processes that evaluate state progress.
- **State-of-the-Art Performance**: Achieves a 50% reduction in the mean absolute error of specialized reasoning baselines, outperforming much larger general MLLMs.
- **Strong Generalization**: Exhibits zero-shot performance on failure detection tasks, achieving 67.0% accuracy on the RoboFail benchmark and surpassing closed-source models like OpenAI o1.
- **Structured Temporal Input**: Explicitly anchors the video sequence between initial and current state images to provide clear goal-oriented context.

## Citations

If you find our work helpful for your research, please consider citing our work.   

```
@misc{liu2026passiveobserveractivecritic,
      title={From Passive Observer to Active Critic: Reinforcement Learning Elicits Process Reasoning for Robotic Manipulation}, 
      author={Yibin Liu and Yaxing Lyu and Daqi Gao and Zhixuan Liang and Weiliang Tang and Shilong Mu and Xiaokang Yang and Yao Mu},
      year={2026},
      eprint={2603.15600},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2603.15600}, 
}
```