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Online Causal Kalman Filtering for Stable and Effective Policy Optimization
🤖AI Summary
Researchers propose Online Causal Kalman Filtering for Policy Optimization (KPO) to address high-variance instability in reinforcement learning for large language models. The method uses Kalman filtering to smooth token-level importance sampling ratios, preventing training collapse and achieving superior results on math reasoning tasks.
Key Takeaways
- →Current reinforcement learning methods for LLMs suffer from high-variance token-level importance sampling that destabilizes training at scale.
- →Local off-policy deviation creates structural inconsistencies at the token level, potentially causing training collapse.
- →KPO applies Kalman filtering to model and update importance sampling ratios across token sequences autoregressively.
- →The method preserves local structure while smoothing noise spikes for more stable policy updates.
- →Experimental results show KPO outperforms state-of-the-art methods on challenging math reasoning datasets.
#reinforcement-learning#large-language-models#kalman-filter#policy-optimization#llm-training#ai-research#machine-learning#stability
Read Original →via arXiv – CS AI
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