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

ArXi:2605.31584v1 Announce Type: cross 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 signals that cannot supervise intermediate reasoning steps. To address these issues, we