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The Choice of Divergence: A Neglected Key to Mitigating Diversity Collapse in Reinforcement Learning with Verifiable Reward
arXiv – CS AI|Long Li, Zhijian Zhou, Jiaran Hao, Jason Klein Liu, Yanting Miao, Wei Pang, Xiaoyu Tan, Wei Chu, Zhe Wang, Shirui Pan, Chao Qu, Yuan Qi||1 views
🤖AI Summary
Researchers have identified a critical flaw in reinforcement learning fine-tuning of large language models that causes degradation in multi-attempt performance despite improvements in single attempts. Their proposed solution, Diversity-Preserving Hybrid RL (DPH-RL), uses mass-covering f-divergences to maintain model diversity and prevent catastrophic forgetting while improving training efficiency.
Key Takeaways
- →Standard RLVR methods suffer from catastrophic forgetting where models lose previously acquired skills during fine-tuning.
- →The choice of divergence term in reinforcement learning objectives has been overlooked as a solution to performance degradation.
- →DPH-RL framework uses forward-KL and JS-divergence to preserve knowledge diversity by continuously referencing the initial policy.
- →The new approach improves both single-attempt and multi-attempt performance while being more computationally efficient.
- →Results demonstrate improved performance on mathematical and SQL generation tasks both in-domain and out-of-domain.
#reinforcement-learning#large-language-models#machine-learning#ai-training#model-optimization#research#performance-improvement#catastrophic-forgetting
Read Original →via arXiv – CS AI
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