Abstract:Search agents lengthen Large Language Models (LLMs) past static parametric information by enabling access to up-to-date and lengthy-tail info unavailable during pretraining. While reinforcement studying has been broadly adopted for coaching such agents, current approaches face key limitations: course of supervision typically suffers from unstable value estimation, whereas final result supervision struggles with credit score task resulting from sparse, trajectory-stage rewards. To bridge this hole, we propose Contribution-Weighted GRPO (CW-GRPO), a framework that integrates process supervision into group relative policy optimization. Instead of immediately optimizing course of rewards, CW-GRPO employs an LLM choose to assess the retrieval utility and reasoning correctness at each search round, producing per-spherical contribution scores. These scores are used to rescale final result-based mostly advantages alongside the trajectory, enabling tremendous-grained credit task with out sacrificing optimization stability. Experiments on a number of information-intensive benchmarks present that CW-GRPO outperforms normal GRPO by 5.0% on Qwen3-8B and 6.3% on Qwen3-1.7B, resulting in more practical search behaviors. Additional analysis reveals that successful trajectories exhibit concentrated contributions in particular rounds, providing empirical perception into search agent duties.

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