Junzhe Wang, Zhiheng Xi, Yajie Yang, Hao Luo, Shihan Dou, Tao Gui, Qi Zhang

Researchers propose Contribution-Weighted GRPO (CW-GRPO), a reinforcement learning framework that improves how LLM-based search agents learn to retrieve and reason over information. The method uses an LLM judge to score the utility of each retrieval step and reasoning correctness, then applies these per-round scores to guide credit assignment across entire trajectories. Experiments show 5-6% improvements over standard GRPO on multiple knowledge-intensive benchmarks, with analysis indicating that successful searches concentrate their value in specific rounds rather than spreading it evenly.