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From $\boldsymbol{\log\pi}$ to $\boldsymbol{\pi}$: Taming Divergence in Soft Clipping via Bilateral Decoupled Decay of Probability Gradient Weight
arXiv – CS AI|Xiaoliang Fu, Jiaye Lin, Yangyi Fang, Chaowen Hu, Cong Qin, Zekai Shao, Binbin Zheng, Lu Pan, Ke Zeng|
🤖AI Summary
Researchers introduce Decoupled Gradient Policy Optimization (DGPO), a new reinforcement learning method that improves large language model training by using probability gradients instead of log-probability gradients. The technique addresses instability issues in current methods while maintaining exploration capabilities, showing superior performance across mathematical benchmarks.
Key Takeaways
- →DGPO uses probability gradients rather than log-probability gradients to solve training instability issues in large language models.
- →The method resolves the conflict between stability and exploration in reinforcement learning with verifiable rewards.
- →Testing across DeepSeek-R1-Distill-Qwen models (1.5B/7B/14B parameters) showed consistent outperformance on mathematical benchmarks.
- →Traditional hard clipping methods stifle exploration while soft clipping methods suffer from divergent weights as probabilities approach zero.
- →The decoupled decay mechanism uses asymmetric, continuous decay based on importance sampling ratios for boundary tokens.
#reinforcement-learning#large-language-models#optimization#ai-training#gradient-methods#machine-learning#research
Read Original →via arXiv – CS AI
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