@inproceedings{dai-etal-2026-every,
title = "Every Step Counts: Step-Level Credit Assignment for Tool-Integrated Text-to-{SQL}",
author = "Dai, Yaxun and
Sun, Baolin and
Wang, Junying and
Wang, Pengfei and
Gao, Yingqi and
Dong, Xuemei and
Chu, Mengdie and
Qi, Xiang and
Chao, Pingfu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.1128/",
pages = "23876--23894",
ISBN = "979-8-89176-462-0",
abstract = "Tool-integrated reasoning has emerged as a promising paradigm for Text-to-SQL, framing SQL generation as sequential decision-making interleaved with tool execution. However, existing reinforcement learning approaches rely on coarse-grained outcome supervision, creating a credit assignment problem: models receive identical rewards regardless of whether intermediate steps are redundant or erroneous. We observe that tool-integrated reasoning offers a unique opportunity: each tool invocation produces structured, verifiable outputs that can serve as training-free step-level signals, eliminating the need for expensive reward model training. Building on this insight, we propose FineStep, a step-level credit assignment framework that leverages training-free process rewards derived from tool execution feedback. FineStep constructs a dual-channel value estimate for each step, combining backward-propagated outcome value with forward-smoothed process value, and synthesizes them via group-based normalization into a unified step-level advantage for policy optimization. Experiments on Text-to-SQL benchmarks show that FineStep achieves SOTA performance with a 3.25{\%} EX gain over GRPO at the 4B scale, while results on AIME24 demonstrate transferability beyond SQL."
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<abstract>Tool-integrated reasoning has emerged as a promising paradigm for Text-to-SQL, framing SQL generation as sequential decision-making interleaved with tool execution. However, existing reinforcement learning approaches rely on coarse-grained outcome supervision, creating a credit assignment problem: models receive identical rewards regardless of whether intermediate steps are redundant or erroneous. We observe that tool-integrated reasoning offers a unique opportunity: each tool invocation produces structured, verifiable outputs that can serve as training-free step-level signals, eliminating the need for expensive reward model training. Building on this insight, we propose FineStep, a step-level credit assignment framework that leverages training-free process rewards derived from tool execution feedback. FineStep constructs a dual-channel value estimate for each step, combining backward-propagated outcome value with forward-smoothed process value, and synthesizes them via group-based normalization into a unified step-level advantage for policy optimization. Experiments on Text-to-SQL benchmarks show that FineStep achieves SOTA performance with a 3.25% EX gain over GRPO at the 4B scale, while results on AIME24 demonstrate transferability beyond SQL.</abstract>
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%0 Conference Proceedings
%T Every Step Counts: Step-Level Credit Assignment for Tool-Integrated Text-to-SQL
%A Dai, Yaxun
%A Sun, Baolin
%A Wang, Junying
%A Wang, Pengfei
%A Gao, Yingqi
%A Dong, Xuemei
%A Chu, Mengdie
%A Qi, Xiang
%A Chao, Pingfu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F dai-etal-2026-every
%X Tool-integrated reasoning has emerged as a promising paradigm for Text-to-SQL, framing SQL generation as sequential decision-making interleaved with tool execution. However, existing reinforcement learning approaches rely on coarse-grained outcome supervision, creating a credit assignment problem: models receive identical rewards regardless of whether intermediate steps are redundant or erroneous. We observe that tool-integrated reasoning offers a unique opportunity: each tool invocation produces structured, verifiable outputs that can serve as training-free step-level signals, eliminating the need for expensive reward model training. Building on this insight, we propose FineStep, a step-level credit assignment framework that leverages training-free process rewards derived from tool execution feedback. FineStep constructs a dual-channel value estimate for each step, combining backward-propagated outcome value with forward-smoothed process value, and synthesizes them via group-based normalization into a unified step-level advantage for policy optimization. Experiments on Text-to-SQL benchmarks show that FineStep achieves SOTA performance with a 3.25% EX gain over GRPO at the 4B scale, while results on AIME24 demonstrate transferability beyond SQL.
%U https://aclanthology.org/2026.findings-emnlp.1128/
%P 23876-23894
Markdown (Informal)
[Every Step Counts: Step-Level Credit Assignment for Tool-Integrated Text-to-SQL](https://aclanthology.org/2026.findings-emnlp.1128/) (Dai et al., Findings 2026)
ACL
- Yaxun Dai, Baolin Sun, Junying Wang, Pengfei Wang, Yingqi Gao, Xuemei Dong, Mengdie Chu, Xiang Qi, and Pingfu Chao. 2026. Every Step Counts: Step-Level Credit Assignment for Tool-Integrated Text-to-SQL. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 23876–23894, Budapest, Hungary. Association for Computational Linguistics.