Are expensive Large Language Model (LLM) fine-tuning methods holding back your specialized agents, demanding massive computational resources and data? We dive into Training-Free Group Relative Policy Optimization (Training-Free GRPO), a novel non-parametric method that enhances LLM agent behavior by distilling semantic advantages from group rollouts into lightweight token priors, eliminating costly parameter updates. Discover how this highly efficient approach achieves significant performance gains in specialized domains like mathematical reasoning and web searching, often surpassing traditional fine-tuning while using only dozens of training samples.

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