From Exploration Collapse to Predictable Limits: Shanghai AI Lab Proposes Entropy-Based Scaling Laws for Reinforcement Learning in LLMs

Recent advances in reasoning-focused large language models have expanded reinforcement learning's capabilities beyond narrow tasks, but this progress faces challenges related to the high computational costs of training and managing policy entropy. Controlling policy entropy through techniques like maximum entropy RL is crucial for balancing exploration and exploitation, yet the application of these methods to LLMs and the development of predictive scaling laws for RL training remain ongoing research areas.