CSC-SQL: Corrective Self-Consistency in Text-to-SQL via Reinforcement Learning

This repository contains the CscSQL-Grpo-Qwen2.5-Coder-7B-Instruct model, presented in the paper CSC-SQL: Corrective Self-Consistency in Text-to-SQL via Reinforcement Learning.

Abstract

Large language models (LLMs) have demonstrated strong capabilities in translating natural language questions about relational databases into SQL queries. In particular, test-time scaling techniques such as Self-Consistency and Self-Correction can enhance SQL generation accuracy by increasing computational effort during inference. However, these methods have notable limitations: Self-Consistency may select suboptimal outputs despite majority votes, while Self-Correction typically addresses only syntactic errors. To leverage the strengths of both approaches, we propose CSC-SQL, a novel method that integrates Self-Consistency and Self-Correction. CSC-SQL selects the two most frequently occurring outputs from parallel sampling and feeds them into a merge revision model for correction. Additionally, we employ the Group Relative Policy Optimization (GRPO) algorithm to fine-tune both the SQL generation and revision models via reinforcement learning, significantly enhancing output quality. Experimental results confirm the effectiveness and generalizability of CSC-SQL. On the BIRD private test set, our 7B model achieves 71.72% execution accuracy, while the 32B model achieves 73.67%.

Code

The official implementation, including training and evaluation scripts, can be found on GitHub: https://github.com/CycloneBoy/csc_sql

Introduction

CSC-SQL is a novel method that integrates Self-Consistency and Self-Correction to enhance SQL generation accuracy. It addresses the limitations of existing test-time scaling techniques by combining their strengths. The method involves selecting the two most frequently occurring outputs from parallel sampling and feeding them into a merge revision model for correction. Furthermore, the Group Relative Policy Optimization (GRPO) algorithm is employed to fine-tune both the SQL generation and revision models via reinforcement learning, leading to significantly enhanced output quality.

The framework overview is illustrated below:

Main Results

The CSC-SQL model achieves state-of-the-art results in Text-to-SQL generation. On the BIRD private test set, the 7B model achieves 71.72% execution accuracy, while the 32B model achieves 73.67%.

Performance Comparison of different Text-to-SQL methods on BIRD dev and test dataset:

Models and Datasets

The project provides various models and datasets, which can be found on Hugging Face and ModelScope:

Usage

You can use this model with the Hugging Face transformers library. Here's a quick example for Text-to-SQL generation following the Qwen chat template:

import torch from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig model_name = "cycloneboy/CscSQL-Grpo-Qwen2.5-Coder-7B-Instruct" tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained( model_name, device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True ).eval() # Example natural language question and a simplified database schema question = "List the names of all employees who work in the 'Sales' department." schema = """ CREATE TABLE employees (  employee_id INT PRIMARY KEY,  name VARCHAR(255),  department_id INT );  CREATE TABLE departments (  department_id INT PRIMARY KEY,  department_name VARCHAR(255) ); """ # Construct the prompt according to the model's expected input format for Text-to-SQL # This is typically a combination of natural language question and the schema user_prompt = f"Question: {question} Schema: {schema} SQL:" messages = [ {"role": "user", "content": user_prompt} ] # Apply the chat template to format the input for the model text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) model_inputs = tokenizer([text], return_tensors="pt").to(model.device) # Define generation configuration generation_config = GenerationConfig( do_sample=True, temperature=0.7, top_p=0.8, top_k=20, repetition_penalty=1.05, max_new_tokens=512, # Adjust as needed for SQL query length eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.pad_token_id, ) # Generate the SQL query generated_ids = model.generate( model_inputs.input_ids, generation_config=generation_config ) # Decode the generated SQL, skipping the input prompt generated_sql = tokenizer.batch_decode(generated_ids[:, model_inputs.input_ids.shape[1]:], skip_special_tokens=True)[0] print("Generated SQL Query:") print(generated_sql) 

Citation

If you find our work helpful or inspiring, please feel free to cite it:

@misc{sheng2025cscsqlcorrectiveselfconsistencytexttosql, title={CSC-SQL: Corrective Self-Consistency in Text-to-SQL via Reinforcement Learning}, author={Lei Sheng and Shuai-Shuai Xu}, year={2025}, eprint={2505.13271}, archivePrefix={arXiv}, primaryClass={cs.CL}, url={https://arxiv.org/abs/2505.13271}, }