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CodeGoat24/
UnifiedReward-Flex-qwen35-9b

Name: UnifiedReward-Flex-qwen35-9b
Availability: InStock

safetensors

qwen3_5

CodeGoat24/UnifiedReward-Flex-SFT-90K

2602.02380

CodeGoat24/UnifiedReward-Think-qwen35-9b

finetune

mit

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Model Summary

UnifiedReward-Flex-qwen35-9b is a unified personalized reward model for vision generation that couples reward modeling with flexible and context-adaptive reasoning!!

[2026/05/13] 🔥🔥 We updated the model weights and enhanced the training data to mitigate the position bias issue!!

For further details, please refer to the following resources:

📰 Paper: https://arxiv.org/abs/2602.02380
🪐 Project Page: https://codegoat24.github.io/UnifiedReward/flex
🤗 Model Collections: https://huggingface.co/collections/CodeGoat24/unifiedreward-flex
🤗 Dataset: https://huggingface.co/datasets/CodeGoat24/UnifiedReward-Flex-SFT-90K
👋 Point of Contact: Yibin Wang

vLLM Server Deployment

export VLLM_DISABLE_FLASHINFER_GDN_PREFILL=1
export TOKENIZERS_PARALLELISM=false
vllm serve CodeGoat24/UnifiedReward-Flex-qwen35-9b \
 --host localhost \
 --port 8080 \
 --trust-remote-code \
 --served-model-name UnifiedReward \
 --gpu-memory-utilization 0.95 \
 --mm-encoder-tp-mode data \
 --mm-processor-cache-type shm \
 --enable-prefix-caching \
 --tensor-parallel-size 8 \
 --default-chat-template-kwargs '{"enable_thinking": false}'

The inference code is provided here.

Citation

bibtex

@article{unifiedreward-flex,
  title={Unified Personalized Reward Model for Vision Generation},
  author={Wang, Yibin and Zang, Yuhang and Han, Feng and Bu, Jiazi and Zhou, Yujie and Jin, Cheng and Wang, Jiaqi},
  journal={arXiv preprint arXiv:2602.02380},
  year={2026}
}