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

This repository contains the models and code for the paper CSC-SQL: Corrective Self-Consistency in Text-to-SQL via Reinforcement Learning.

📖 Paper on arXiv | 💻 GitHub Repository

Introduction

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%.

Main Results

Performance Comparison of different Text-to-SQL methods on BIRD dev and test dataset. On the BIRD private test set, our 7B model achieves 71.72% execution accuracy, while the 32B model achieves 73.67%.

Model Checkpoints

The available models are fine-tuned on Qwen2.5-Coder and are accessible on Hugging Face:

Quickstart

You can use this model with the transformers library. The config.json indicates Qwen2ForCausalLM as its architecture.

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig

model_name = "cycloneboy/CscSQL-Grpo-Qwen2.5-Coder-7B-Instruct"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
model.eval()

# Example usage for text-to-SQL generation
question = "List the names of all employees."
db_schema = "CREATE TABLE employees (id INT, name TEXT, salary INT);"
prompt = f"Question: {question}
Schema: {db_schema}
SQL: "

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

generation_config = GenerationConfig(
    max_new_tokens=128,
    do_sample=True,
    temperature=0.7,
    top_k=20,
    top_p=0.8,
    repetition_penalty=1.05,
    eos_token_id=tokenizer.eos_token_id,
    pad_token_id=tokenizer.pad_token_id,
)

with torch.no_grad():
    outputs = model.generate(
        **inputs,
        generation_config=generation_config
    )

response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
print(f"Generated SQL: {response}")

Citation

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

@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}, 
}