SCOPE-RL: Stable and Quantitative Control of Policy Entropy in RL Post-Training

Name: SCOPE-RL: Stable and Quantitative Control of Policy Entropy in RL Post-Training
Uploaded: 2026-10-23T08:14:54.844628+00:00
Duration: 374 s
Description: SCOPE-RL is a framework that stabilizes Reinforcement Learning for large language models by controlling policy entropy collapse. It uses temperature-adaptive positive samples to counteract premature convergence, promoting exploration when entropy is low and maintaining stability when it is too high. This method improves reasoning performance significantly over standard methods like GRPO.

SCOPE-RL is a framework that stabilizes Reinforcement Learning for large language models by controlling policy entropy collapse. It uses temperature-adaptive positive samples to counteract premature convergence, promoting exploration when entropy is low and maintaining stability when it is too high. This method improves reasoning performance significantly over standard methods like GRPO.

Open the full episode