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This study compares two LLM-based tutoring approaches (Socratic guidance vs prompt refinement) for programming education, finding that Socratic guidance fosters better learning outcomes and more understanding-driven prompting strategies when students later use unconstrained LLMs.
This paper formalizes communication policy for LLM agents and proposes Communication Policy Evolution (CPE), a self-evolution framework that refines communication policies through rollout and prompt-level evolving, achieving best task success across multiple settings.
PACE introduces a two-timescale framework for self-evolution of small language model agents, coordinating low-risk prompt refinement with higher-risk control-logic updates, achieving up to +9.2% relative improvement across benchmarks.