Role-Based LLM Interaction Boosts Coding Performance While Cutting Tokens
Researchers propose PETITE, a multi-agent framework where two instances of the same LLM take on asymmetric tutor and student roles to solve coding problems more effectively than existing approaches. The student agent generates and refines solutions while the tutor provides structured feedback without access to ground truth, mimicking human cognitive development through role-based interaction. Tested on the APPS coding benchmark against Self-Consistency, Self-Refine, Multi-Agent Debate, and Multi-Agent Review, PETITE achieves comparable or better accuracy while using significantly fewer tokens, suggesting that role-differentiated interaction structures can enhance problem-solving efficiency without requiring stronger models or heterogeneous ensembles.