On Information Self-Locking in Reinforcement Learning for Active Reasoning of LLM agents

This paper explores the phenomenon of "information self-locking" in reinforcement learning for active reasoning in Large Language Model (LLM) agents. It investigates how LLM agents might get stuck in suboptimal reasoning loops and proposes methods to overcome these limitations for improved active reasoning.