Reliable Text-to-SQL in Resource-Constrained Settings with Small LLMs via Iterative Retry Workflows

Bilel Abderrahmane Benziane; Xavier Schepler

cs.AIOct 12, 2026

Reliable Text-to-SQL in Resource-Constrained Settings with Small LLMs via Iterative Retry Workflows

Authors: Bilel Abderrahmane Benziane, Xavier Schepler

Organizations: Data Lab, Generix Group, Paris, France

Abstract

Large Language Models (LLMs) have recently improved Text-to-SQL generation, but their computational cost can hinder deployment in resource-constrained settings. This paper evaluates the Text-to-SQL capabilities of small and medium open-source LLMs (1B-8B parameters) under a controlled inference budget, focusing on how model specialization and prompt formulation affect performance. Using the Spider benchmark, we compare multiple prompt representations and assess the accuracy of generated queries. Our results show that SQL-specialized models are consistently more reliable than general-purpose and chat-oriented models at small scales, and that performance can vary substantially depending on the prompt used. To reduce this variability without resorting to larger models, we introduce three lightweight inference workflows that automatically retry with alternative prompts and/or models when failures occur, including an optional syntax-correction step. These workflows significantly improve end-to-end success rates, with a trade-off in additional inference calls. These results demonstrate a practical path to stronger Text-to-SQL performance with small LLMs, by trading modest additional inference cost for higher reliability.

Figures & tables

Fig. 1: Distribution of SQL query types in the Spider evaluation set.

Type	Model ID	Alpaca Sft		Basic Prompt		Code Repr.		OpenAI Demo		Struct. Prompt		Text Repr.		Avg
		EM	EX	EM	EX	EM	EX	EM	EX	EM	EX	EM	EX	EM	EX
Fund.	Llama-3-8B	21.86	70.50	2.32	33.08	28.53	3.10	29.21	6.29	25.15	74.95	25.05	39.27	22.02	37.86
	Qwen2.5-7B	31.43	87.81	4.45	27.66	39.27	43.42	37.91	30.37	37.23	83.08	37.91	90.43	31.37	60.46
	Mistral-7B	21.95	27.27	2.03	27.47	32.01	17.70	29.01	0.19	3.58	0.00	31.43	0.19	20.00	12.14
	Llama-2-7B	9.67	0.77	0.77	16.83	14.99	1.06	12.19	0.77	0.00	0.00	12.09	0.58	8.29	3.34
	Deepseek-7B	16.34	0.77	2.71	26.50	23.99	0.58	21.57	0.19	20.02	0.00	20.21	0.10	17.47	4.69

TABLE I: Model performance (EM/EX) across prompt styles on the Spider evaluation set.

Fig. 3: Radar plots of EM (green) and EX (blue) across query categories. (a) Baseline single-pass inference with different prompt styles. (b) Model-Flow workflow. (c) Prompt-Flow workflow. (d) Model-and-Prompt Flow workflow.

Algorithm	Repr	EX	EM
Prompt workflow (Prem-1B-SQL)	–	97.20	41.30
Prompt workflow (CodeS-1B-Spider)	–	87.81	43.13
Model and prompt workflow	–	97.20	41.30
Model workflow	Alpaca Sft	76.69	7.83
	Basic Prompt	58.99	16.34
	Code Repr.	62.96	23.89

TABLE II: Workflow Accuracy Comparison (EX / EM)

Text-to-SQL

Text-to-SQL translates natural-language questions into executable database queries, linking user intent to schemas, values, and SQL logic.

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Large language models have driven major advances in Text-to-SQL generation. However, they suffer from high computational cost, long latency, and data privacy concerns, which make them impractical for many real-world applications. A natural alternative is to use small language models (SLMs), which enable efficient and private on-premise deployment. Yet, SLMs often struggle with weak reasoning and poor instruction following. Conventional reinforcement learning methods based on sparse binary rewards (0/1) provide little learning signal when the generated SQLs are incorrect, leading to unstable or collapsed training. To overcome these issues, we propose FINER-SQL, a scalable and reusable reinforcement learning framework that enhances SLMs through fine-grained execution feedback. Built on group relative policy optimization, FINER-SQL replaces sparse supervision with dense and interpretable rewards that offer continuous feedback even for incorrect SQLs. It introduces two key reward functions: a memory reward, which aligns reasoning with verified traces for semantic stability, and an atomic reward, which measures operation-level overlap to grant partial credit for structurally correct but incomplete SQLs. This approach transforms discrete correctness into continuous learning, enabling stable, critic-free optimization. Experiments on the BIRD and Spider benchmarks show that FINER-SQL achieves up to 67.73% and 85% execution accuracy with a 3B model -- matching much larger LLMs while reducing inference latency to 5.57~s/sample. These results highlight a cost-efficient and privacy-preserving path toward high-performance Text-to-SQL generation. Our code is available at https://github.com/thanhdath/finer-sql.

Abstract

Text-to-SQL

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