LongTraceRL: Learning Long-Context Reasoning from Search Agent Trajectories with Rubric Rewards
arXiv:2605.31584v1 Announce Type: cross 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 signals that cannot supervise intermediate reasoning steps. To address these issues, we introduce \textsc{LongTraceRL}. For data construction, we generate multi-hop questions via knowledge graph r
The continuous drive for more capable LLMs is pushing research into complex reasoning tasks, with limitations in long-context understanding becoming a primary bottleneck.
Improving long-context reasoning directly enhances the utility and autonomy of AI systems, enabling them to tackle more sophisticated problems requiring deep information integration.
This research introduces a novel approach to training LLMs for complex, multi-step reasoning by generating more effective supervision signals from search agent trajectories and rubric rewards.
- · AI researchers
- · Large Language Model developers
- · SaaS companies leveraging AI
- · Data scientists
- · AI models reliant on short-context processing
- · Manual data integration workflows
Further advancements in LLM capabilities for abstract and multi-step tasks, reducing human intervention.
Acceleration in the development of more autonomous and intelligent AI agents capable of complex problem-solving.
Enhanced AI systems could lead to breakthroughs in scientific discovery and automated knowledge work across various industries.
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Read at arXiv cs.LG