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When Does External Guidance Help LLM Reasoning? A Bias-Variance Theory of Guidance-Augmented GRPO
Not provided in the abstract
guidance-augmentedpolicy-gradientreinforcement learningreasoning
2610.06861
Builder Relevance
1h ago80%
Abstract
The paper presents a theoretical framework for understanding the impact of external guidance on large language model reasoning.
Reality Card
Core Claim
The GA-GRPO framework achieves convergence at a rate of O(1/sqrt(T)) and provides an optimal guidance weight that improves performance while reducing GPU resource usage by 31%.
Method / Result
GA-GRPO matches or surpasses existing methods while requiring 31% fewer GPU-hours.
Limitations
The paper does not provide convergence rates or bias bounds for all methods discussed, which may affect reproducibility.
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