distractors

LongTraceRL introduces tiered distractor construction and rubric reward design to improve long-context reasoning in language models using reinforcement learning. The method generates multi-hop questions via knowledge graph random walks and uses search agent trajectories to build challenging distractors, with a rubric reward providing entity-level process supervision.