TACT: Alleviating Credit Misassignment in Group-Based Policy Optimization for Search Agents with Tool-Aware Credit Transfer

Mainstream group-based policy optimization methods broadcast trajectory-level advantages to all tokens, potentially penalizing useful steps in failed trajectories as heavily as unhelpful ones. We refer to this over-penalization of useful tool calls as credit misassignment. In search scenarios, many tool calls in failed trajectories have parameters similar to those of calls in successful trajectories for the same question, providing a signal for identifying and mitigating credit misassignment. Building on this insight, we propose Tool-Aware Credit Transfer (TACT), which leverages the parameter–intent correspondence of information-acquisition tools: similar call parameters provide probabilistic evidence of similar information-seeking intent, guiding conservative credit transfer. TACT constructs counterfactual references within the current training batch and attenuates potentially misassigned negative credit via confidence-gated conservative advantage correction. It requires no additional annotation, models, or sampling during training, and introduces negligible computational overhead. Across multiple search benchmarks, TACT delivers consistent, plug-and-play improvements over strong baselines for three group-based policy optimization algorithms (GRPO, GSPO, and SAPO). Our code and models will be publicly released upon acceptance.