Reinforcement Learning

The G-CARL framework addresses the challenge of medical factuality in automated report interpretation by verifying individual claims against authoritative medical knowledge. It utilizes a dual-verification process and clinician-weighted checklists to ensure responses are both medically accurate and contextually relevant.

This technical excerpt provides a formal mathematical proof for the stability and convergence properties of the Co-RL framework. It specifically shows how collaborative dynamics enlarge the basin of attraction for correct outcomes in multi-agent reinforcement learning.

This technical paper proposes a new distillation paradigm called Direct-OPD to improve weak-to-strong generalization in reasoning models. By distilling only the policy shift induced by reinforcement learning, the method avoids the capacity limitations inherent in traditional teacher-student imitation.