New RL Framework Exploration-Distillation Boosts LLM Exploration, Outperforms DAPO
micahgoldblum · x · 2026-10-13
Researcher Micah Goldblum announced Exploration-Distillation (ExpDis), an RL framework designed to unlock aggressive exploration in LLMs during reinforcement learning, delivering large performance gains over standard algorithms like DAPO.
Core idea: while LLMs can in principle discover new things during RL, existing algorithms struggle. ExpDis insulates the student model from the explorer's degradation, preserving general capabilities while improving exploration diversity.
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