LongTraceRL: Learning Long-Context Reasoning from Search Agent Trajectories with Rubric Rewards 事件

LongTraceRL: Learning Long-Context Reasoning from Search Agent Trajectories with Rubric Rewards arXiv:2605.31584v1 Announce Type: new Abstract: Long-context reasoning remains a central challenge for large language models, which often fail to locate and integrate key information in extensive distracting content. Reinforcement learning with verifiable rewards (RLVR) has shown promise for this task, yet existing methods are limited by low-confusability distractors and sparse, outcome-only reward s