Beyond the 80/20 Rule: High-Entropy Minority Tokens Drive EffectiveReinforcement Learning for LLM Reasoning

This content explores a novel approach to improve Reinforcement Learning for Large Language Model (LLM) reasoning by focusing on "high-entropy minority tokens". It proposes that these less frequent yet highly informative tokens are key drivers for effective learning, challenging the conventional 80/20 rule.