Think Twice Before Calling Tools: How Pre-Call Reasoning in SFT Shapes Tool-Use Retention under RL

Tool use learned during supervised fine-tuning (SFT) is not always retained during reinforcement learning (RL). We study how properties of SFT data may affect this retention by constructing Low, Medium, and High SFT datasets with approximately matched token budgets but different tool-call depths. Under the same RL recipe, the three initializations reach similar RL training accuracy yet exhibit markedly different tool-use dynamics: tool initiation increases in Low, declines in Medium, and collapses in High. We observe that these datasets also differ substantially in the amount of reasoning preceding each tool call. In particular, High trajectories contain considerably less pre-call reasoning, motivating the hypothesis that pre-call reasoning may affect how well tool use is calibrated and, consequently, whether the corresponding behaviors are retained during RL. An idealized theoretical analysis provides a selective-retention view of this process: advantages in overall and tool-conditioned accuracy can be preserved under outcome-only optimization, while tool initiation increases only when tool-using trajectories are more successful than the tool-free alternative. Guided by this analysis, we rank the original High trajectories by average pre-call reasoning length and retain the highest-ranked examples while approximately matching cumulative SFT token exposure. This simple filtering strategy improves post-RL accuracy and tool-conditioned final-answer success without consistently increasing tool initiation across multiple benchmarks. These results suggest that pre-call reasoning is a useful dimension of SFT supervision for understanding and improving tool-use retention under downstream RL.