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DIVA-GRPO: Enhancing Multimodal Reasoning through Difficulty-Adaptive Variant Advantage
arXiv – CS AI|Haowen Gao, Zhenyu Zhang, Liang Pang, Fangda Guo, Hongjian Dou, Guannan Lv, Shaoguo Liu, Tingting Gao, Huawei Shen, Xueqi Cheng||3 views
🤖AI Summary
Researchers have developed DIVA-GRPO, a new reinforcement learning method that improves multimodal large language model reasoning by adaptively adjusting problem difficulty distributions. The approach addresses key limitations in existing group relative policy optimization methods, showing superior performance across six reasoning benchmarks.
Key Takeaways
- →DIVA-GRPO solves sparse rewards and advantage vanishing problems in current GRPO methods for multimodal large language models.
- →The method dynamically assesses problem difficulty and samples variants with appropriate difficulty levels for better training.
- →Extensive experiments on six reasoning benchmarks demonstrate superior training efficiency and reasoning performance.
- →The approach uses difficulty-weighted and normalized scaling across local and global groups to improve training stability.
- →Code has been made publicly available on GitHub for research community adoption.
#reinforcement-learning#multimodal#large-language-models#reasoning#grpo#machine-learning#ai-research#optimization
Read Original →via arXiv – CS AI
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