What is the impact of automated hyperparameter tuning strategies on the reasoning capabilities of large langua

Abstract

Abstract: Reinforcement learning (RL) has become a key technique for enhancing the reasoning abilities of large language models (LLMs), with policy-gradient algorithms dominating the post-training stage because of their efficiency and effectiveness. However, most existing benchmarks evaluate large-language-model reasoning under idealized settings, overlooking performance in realistic, non-ideal scenarios. We identify three representative non-ideal scenarios with practical relevance: summary inference, fine-grained noise suppression, and contextual filtering. We introduce a new research direction guided

Research Question

What is the impact of automated hyperparameter tuning strategies on the reasoning capabilities of large language models across diverse logical inference datasets?

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