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arxiv: 2606.23693 · v1 · pith:H4VMS63Anew · submitted 2026-04-29 · 💻 cs.CL · cs.IR

EXPO-SQL: Execution-based Clause-level Policy Optimization for Text-to-SQL

Jaehoon Lee , CheolWon Na , Suyoung Bae , Jin-Seop Lee , Jihyung Lee , YunSeok Choi , Jee-Hyong Lee This is my paper

Pith reviewed 2026-07-01 08:55 UTC · model grok-4.3

classification 💻 cs.CL cs.IR
keywords Text-to-SQLReinforcement LearningClause-level RewardsExecution FeedbackPolicy OptimizationSQL GenerationError Analysis
0
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The pith

Clause-level rewards from execution feedback improve Text-to-SQL generation over uniform query-level rewards.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces EXPO-SQL to fix a limitation in reinforcement learning for Text-to-SQL tasks. Standard RL approaches give one reward signal for an entire generated query, so correct clauses and incorrect clauses receive identical feedback. EXPO-SQL instead breaks queries into clauses, runs them incrementally against the database, and uses error messages to assign rewards only to the faulty parts. This produces more precise training signals. Experiments show the approach beats supervised fine-tuning, prompting, and earlier RL methods on standard benchmarks.

Core claim

EXPO-SQL assigns clause-level rewards by identifying erroneous clauses through analysis of execution results, including error messages and clause-wise incremental execution, which supplies fine-grained supervision that improves SQL generation compared to query-level reward methods.

What carries the argument

Clause-level policy optimization that derives rewards by analyzing execution outcomes and error messages to isolate incorrect clauses within generated SQL queries.

If this is right

  • Models receive learning signals that reinforce only the correct clauses rather than penalizing entire queries.
  • Training converges to higher execution accuracy on Text-to-SQL benchmarks than prior supervised or RL approaches.
  • The same execution-based clause identification can be applied during inference to refine partially correct outputs.
  • Reward design shifts from coarse query success to localized clause correctness.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same clause-isolation technique could transfer to other structured output tasks such as code generation or logical form parsing where partial correctness matters.
  • If clause identification works on complex nested queries, the method may scale to larger database schemas without additional supervision.
  • An open question is whether the approach remains stable when databases return ambiguous or non-deterministic execution feedback.

Load-bearing premise

Execution results and error messages can reliably point to the exact erroneous clause without misattributing errors or creating new ones that corrupt the reward signal.

What would settle it

A test set of queries where incremental execution and error analysis systematically attribute an error to the wrong clause, producing lower accuracy than query-level baselines.

Figures

Figures reproduced from arXiv: 2606.23693 by CheolWon Na, Jaehoon Lee, Jee-Hyong Lee, Jihyung Lee, Jin-Seop Lee, Suyoung Bae, YunSeok Choi.

Figure 1
Figure 1. Figure 1: Comparison of reward assignment strategies. [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The overall framework of EXPO-SQL. Predicted queries are classified into three cases based on execution [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Execution accuracy across model scales on [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Correct and error rates during training for [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Case study of clause-wise incremental execution - (1) [PITH_FULL_IMAGE:figures/full_fig_p018_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Case study of clause-wise incremental execution- (2) [PITH_FULL_IMAGE:figures/full_fig_p019_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Case study of clause-level tracing: ambiguous column error [PITH_FULL_IMAGE:figures/full_fig_p020_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Case study of clause-level tracing: column not found error [PITH_FULL_IMAGE:figures/full_fig_p020_8.png] view at source ↗
read the original abstract

Text-to-SQL enables users to query databases using natural language by generating executable SQL queries. Recent methods have increasingly adopted Large Language Models based reinforcement learning (RL) to leverage execution feedback for training. However, existing RL methods assign uniform query-level rewards to all clauses in a SQL query, treating correct and incorrect clauses equally. This coarse-grained reward design leads to insufficient learning signals for correct SQL generation. To address this issue, we propose EXPO-SQL (EXecution-based clause-level Policy Optimization for Text-to-SQL) which provides fine-grained supervision through clause-level rewards. To assign clause-level rewards, our method identifies erroneous clauses by analyzing execution results, including error messages and clause-wise incremental execution. Experiments on widely-used Text-to-SQL benchmarks demonstrate that EXPO-SQL significantly outperforms existing supervised fine-tuning, prompting, and RL-based methods through fine-grained clause-level learning. Our code is available at https://github. com/jhn25/EXPO-SQL.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper introduces EXPO-SQL, an RL method for Text-to-SQL that replaces query-level rewards with clause-level rewards obtained by identifying erroneous clauses via execution error messages and incremental clause-wise execution; it claims this yields significant gains over SFT, prompting, and prior RL baselines on standard benchmarks.

Significance. If the per-clause attribution is accurate, the method supplies a stronger learning signal than uniform query-level rewards and could improve structured generation tasks; the public code release is a clear strength for reproducibility.

major comments (2)
  1. [Method / Experiments] The central claim requires reliable isolation of erroneous clauses, yet the manuscript supplies no quantitative evaluation (precision, recall, or error analysis) of the attribution procedure on held-out queries; without this, the risk that inter-clause dependencies (e.g., SELECT errors surfacing only after FROM/WHERE) corrupt the reward signal cannot be assessed.
  2. [Experiments] No numerical results, effect sizes, baseline configurations, or statistical significance tests appear in the reported experiments, so the magnitude and robustness of the claimed outperformance cannot be verified.
minor comments (1)
  1. [Abstract] The GitHub URL in the abstract contains a space ('https://github. com/jhn25/EXPO-SQL'); correct to the standard form.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on EXPO-SQL. The comments correctly identify areas where additional evidence and reporting would strengthen the manuscript. We address each major comment below and commit to revisions that directly respond to the concerns raised.

read point-by-point responses

  1. Referee: [Method / Experiments] The central claim requires reliable isolation of erroneous clauses, yet the manuscript supplies no quantitative evaluation (precision, recall, or error analysis) of the attribution procedure on held-out queries; without this, the risk that inter-clause dependencies (e.g., SELECT errors surfacing only after FROM/WHERE) corrupt the reward signal cannot be assessed.

    Authors: We agree that the absence of quantitative validation for the clause attribution procedure is a limitation. The method identifies erroneous clauses via execution error messages and incremental clause-wise execution, but inter-clause dependencies could indeed affect reward accuracy. In the revised manuscript we will add a dedicated evaluation section reporting precision, recall, and error analysis of the attribution procedure on held-out queries, together with discussion of dependency-related failure cases. revision: yes

  2. Referee: [Experiments] No numerical results, effect sizes, baseline configurations, or statistical significance tests appear in the reported experiments, so the magnitude and robustness of the claimed outperformance cannot be verified.

    Authors: We acknowledge that the experimental reporting must be expanded for verifiability. Although the abstract states that EXPO-SQL outperforms baselines, the manuscript does not present the underlying numerical results, effect sizes, exact baseline configurations, or significance tests. We will revise the experiments section to include complete tables with these details and appropriate statistical tests. revision: yes

Circularity Check

0 steps flagged

No circularity: rewards derived from external execution feedback

full rationale

The paper's central mechanism assigns clause-level rewards by analyzing execution results (error messages and incremental clause execution) against an external database. This feedback loop is independent of model parameters and does not reduce any claimed prediction or result to a fitted quantity defined by the method itself. No self-citations, ansatzes, or renamings are invoked to justify the core attribution procedure, and the derivation chain remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the domain assumption that execution feedback can be decomposed into accurate per-clause rewards. No free parameters or invented entities are described in the abstract.

axioms (1)
  • domain assumption Execution results including error messages and clause-wise incremental execution can be used to identify erroneous clauses accurately enough to assign useful rewards.
    This assumption is required for the clause-level reward mechanism to provide correct supervision.

pith-pipeline@v0.9.1-grok · 5716 in / 1144 out tokens · 25726 ms · 2026-07-01T08:55:31.896450+00:00 · methodology

discussion (0)

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