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:

Model and Dataset	Modelscope	HuggingFace
bird train and dev dataset	🤖 Modelscope	🤗 HuggingFace
CscSQL-Merge-Qwen2.5-Coder-3B-Instruct	🤖 Modelscope	🤗 HuggingFace
CscSQL-Merge-Qwen2.5-Coder-7B-Instruct	🤖 Modelscope	🤗 HuggingFace
CscSQL-Grpo-Qwen2.5-Coder-3B-Instruct	🤖 Modelscope	🤗 HuggingFace
CscSQL-Grpo-XiYanSQL-QwenCoder-3B-2502	🤖 Modelscope	🤗 HuggingFace
CscSQL-Grpo-Qwen2.5-Coder-7B-Instruct	🤖 Modelscope	🤗 HuggingFace
CscSQL-Grpo-XiYanSQL-QwenCoder-7B-2502	🤖 Modelscope	🤗 HuggingFace

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:

python

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:

bibtex

@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}, 
}

cycloneboy/CscSQL-Merge-Qwen2.5-Coder-3B-Instruct

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

Abstract