ECHO: Entropy-Confidence Hybrid Optimization for Test-Time Reinforcement Learning 事件

ECHO: Entropy-Confidence Hybrid Optimization for Test-Time Reinforcement Learning arXiv:2602.02150v2 Announce Type: replace-cross Abstract: Test-time reinforcement learning generates multiple candidate answers via repeated rollouts and performs online updates using pseudo-labels constructed by majority voting. To reduce overhead and improve exploration, prior work introduces tree structured rollouts, which share reasoning prefixes and branch at key nodes to improve sampling efficiency. However,