Every Step Counts: Step-Level Credit Assignment for Tool-Integrated Text-to-SQL
Tool-integrated Text-to-SQL parsing has emerged as a promising paradigm, framing SQL generation as a sequential decision-making process interleaved with tool execution. However, existing reinforcement learning approaches mainly rely on coarse-grained outcome supervision, resulting in a fundamental credit assignment problem: models receive the same reward for any trajectory that yields the correct answer, even when intermediate steps are redundant, inefficient, or erroneous. Consequently, models are encouraged to explore suboptimal reasoning spaces, limiting both efficiency and generalization. To address this problem, we propose FineStep, a novel framework for step-level credit assignment ...
| Authors | Yaxun Dai, Baolin Sun, Junying Wang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 6 May 2026 |
| Last updated at source | 7 May 2026 |
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