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title: OraRL
emoji: 🎬
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colorTo: purple
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# OraRL

[[🏠 Homepage](https://orarl.github.io/)] [[📖 Arxiv Paper](https://arxiv.org/abs/2608.20492)] [[🤗 Models & Data](https://huggingface.co/OraRL)] [[💻 Code](https://github.com/HVision-NKU/OraRL)]

We build **OraRL**, a reinforcement-learning framework for **unified video multimodal large language models**.

Our goal is to make broad video RL both **reliable** and **efficient** by treating task annotations as positive rollouts while retaining policy samples for the on-policy baseline. The resulting **Video-ORA** family handles seven task families without chain-of-thought decoding.

<video controls playsinline preload="metadata" width="100%" src="https://orarl.github.io/assets/orarl-teaser.mp4"></video>

<p align="center"><strong>▶ OraRL project overview (1:38)</strong></p>

## What we release

- **Video-ORA-9B**: a unified video MLLM for temporal and spatial grounding, segmentation, tracking, video QA, and spatial intelligence.
- **OraRL-Data**: canonical evaluation annotations and raw media for reproducible comparison across the seven task families.
- **OraRL resources**: project documentation, model cards, inference examples, and evaluation guidance.

## Research interests

- Unified video understanding
- Multimodal reinforcement learning
- Temporal & spatial grounding
- Video segmentation, tracking & spatial intelligence