Xingyang li

Reinforcement Learning with Verifiable Rewards (RLVR) has become the standard paradigm for enhancing reasoning capabilities in Large Language Models, yet on-policy algorithms like GRPO suffer from sample inefficiency. Current experience replay methods for RLVR typically replay correct trajectorie...

Reinforcement Learning with Verifiable Rewards (RLVR) frequently suffers from mode collapse due to the inherent sparsity of feedback signals. While strategies such as entropy regularization introduce randomness, they lack directionality. Simply incorporating diversity rewards is overly one-sided...