Brief

Researchers have developed Extreme Region Policy Distillation (ERPD), a novel two-stage framework for reinforcement learning in large language models. This method aims to overcome the trade-off between sample efficiency and asymptotic performance by decoupling these aspects. The first stage uses weakly constrained off-policy optimization to extract maximum training signals from fixed data, providing token-level supervision. The second stage distills these signals into a base policy under trust-region constraints, filtering harmful drift while preserving useful information. AI