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OraRL
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Model Details
Model Provider
OraRL
Model Tree
Qwen/Qwen3.5-9B
Input Modalities
Output Modalities
Supported Functionality
| Property | Value |
|---|---|
| Architecture | Qwen3_5ForConditionalGeneration |
| Parameters | 9B |
| Weight dtype | BF16 |
| Native context length | 262,144 tokens |
| Base model | Qwen/Qwen3.5-9B |
| Post-training | OraRL annotation-augmented on-policy reinforcement learning |
| Tested serving | Transformers 5.5.4 and vLLM 0.19.1 |
Video-ORA-9B leads the matched seven-family comparison without chain-of-thought
decoding. Best and second-best values are highlighted per row; † denotes an
original-report value whose frame, prompt, split, or decoding settings may
differ. Averages require complete family coverage.
See the OraRL repository, project page, and paper for complete benchmark protocols and source attribution.
bash
pip install "vllm==0.19.1" openaivllm serve OraRL/Video-ORA-9B \--served-model-name Video-ORA-9B \--port 8000 \--tensor-parallel-size 1 \--max-model-len 262144 \--reasoning-parser qwen3 \--media-io-kwargs '{"video": {"num_frames": -1}}' \--limit-mm-per-prompt '{"image": 1, "video": 1}'
Reduce --max-model-len if KV-cache memory is limited. Increase
--tensor-parallel-size for multi-GPU serving. Model-weight loading occupies
approximately 17.6 GiB in the tested BF16 vLLM environment; this is not a
full peak-memory measurement.
Send an OpenAI-compatible video request:
python
from openai import OpenAIclient = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")response = client.chat.completions.create(model="Video-ORA-9B",messages=[{"role": "user","content": [{"type": "video_url","video_url": {"url": "https://orarl.github.io/assets/orarl-teaser.mp4"},},{"type": "text","text": "Describe the video and answer the question directly.",},],}],max_tokens=128,temperature=0.0,extra_body={"top_k": 20,"chat_template_kwargs": {"enable_thinking": False},"mm_processor_kwargs": {"fps": 2, "do_sample_frames": True},},)print(response.choices[0].message.content)
Replace the demo URL with your own accessible video URL. For local files,
launch vLLM with an appropriate --allowed-local-media-path.
Qwen3.5 requires a recent Transformers version:
bash
pip install "transformers[serving] @ git+https://github.com/huggingface/transformers.git@main"pip install accelerate torchvision pillowtransformers serve \--force-model OraRL/Video-ORA-9B \--port 8000 \--continuous-batching
The checkpoint also contains the tokenizer, processor configuration, generation configuration, and chat template required by compatible Transformers and vLLM releases. Task-specific prompts and output schemas are documented on the project page.
Video-ORA-9B is intended for research on structured image/video perception,
benchmark evaluation, and task-specific adaptation. Use direct answer prompts
with enable_thinking=False to match the reported evaluation protocol.
Out-of-scope uses include safety-critical decisions, identity inference, surveillance deployment, or use that violates the licenses or consent requirements of upstream media.
Training uses public training splits from the task families described in the paper. Evaluation identities, questions, and media anchors are excluded during mixture construction. Dataset and media licenses remain governed by their original sources; no training or benchmark media are distributed with this checkpoint.
Video-ORA-9B is a research checkpoint optimized for structured video and spatial-understanding tasks. It may produce malformed task-specific outputs, hallucinate visual details, or inherit limitations and biases from its base model and training data. It has not been validated for safety-critical or high-stakes use.
The checkpoint is released under the Apache License 2.0. It is derived from Qwen3.5-9B, which is also distributed under Apache 2.0.
bibtex
@article{li2026orarl,title = {Annotations as Rollouts: Efficient and ScalableReinforcement Learning for Video MLLMs},author = {Li, Yunheng and Mu, Guohong and Li, Hao andQian, Shengsheng and Zhang, Dingwen and Hou, Qibinand Cheng, Ming-Ming},journal = {arXiv preprint arXiv:2608.20492},year = {2026},url = {https://arxiv.org/abs/2608.20492}}