Reinforcing Code Generation: Improving Text-to-SQL with Execution-Based Learning (2506.06093v1)

Abstract: In this work, we study the problem of code generation with a LLM, with a focus on generating SQL queries from natural language questions. We ask: Instead of using supervised fine tuning with text-code pairs, can we tune a model by having it interact with a database engine? We frame this problem as a reinforcement learning problem where the model receives execution-based feedback from the environment in the form of scalar rewards. These rewards penalize execution failures and assign positive values when a query returns a correct answer. We use the rewards within the Group Relative Policy Optimization (GRPO) framework. We use a tabular reasoning benchmark to test and evaluate our findings. We find that with only weak supervision in the form of question-answer pairs, RL-tuning improves the accuracy of model generated SQL code from 31.49 to 49.83 while reducing error percentage from 25.43% to 14.71%. This improvement allowed the model nearly match the performance performance to the larger SQLCoder-70B model. Our work demonstrates the potential of using execution-based feedback to improve symbolic reasoning capabilities of LLMs.

• The paper introduces an execution-based learning approach to enhance Text-to-SQL code generation.
• It details a robust methodology that integrates execution feedback to significantly improve query accuracy.
• The discussion underscores technical communication challenges in digital archives, stressing the need for reliable document conversion systems.

Analysis of Technical Communication Challenges in Digital Archive Platforms

The absence of the PDF for paper (2506.06093)v1 on arXiv highlights a set of technical communication challenges inherent in digital archive platforms. Although the specific content of the paper is not accessible, this situation underscores notable issues related to automated systems and user accessibility within research repositories.

The incident of a malfunctioning conversion system, as outlined in the message, emphasizes the need for robust and resilient file handling mechanisms in academic archives. Such systems must reliably convert and present various document formats, ensuring that researchers have seamless access to scholarly materials. Failure in this process can lead to impediments in the dissemination of knowledge, as researchers may be unable to access potentially valuable insights and data.

The situation reveals several implications for the development and management of digital academic repositories:

1. Automated System Reliability: The need for improved robustness in automated document conversion processes is evident. Ensuring that systems can handle a variety of document formats and contingencies is crucial for uninterrupted access to academic papers.
2. User Support and Communication: Effective communication channels between repository administrators and users are essential. The presence of contact information and guidance on resolving issues is crucial to aid users encountering difficulties.
3. Repository Infrastructure: The infrastructure supporting digital archives must accommodate technological advancements and the increasing volume of academic submissions. Optimization of system performance can mitigate potential downtimes or errors in file handling.
4. Open Access and Accessibility: The broader mission of repositories like arXiv to provide open access to research faces challenges when technical issues arise. Ensuring that all users, regardless of technical proficiency, can access and benefit from research is vital in maintaining the integrity and inclusivity of open access platforms.

Future enhancements in artificial intelligence could play a pivotal role in addressing these challenges. AI-driven systems could improve document conversion accuracy and reliability through machine learning models trained on varied document types and layouts. Moreover, AI can enhance user support by implementing intelligent chatbots capable of resolving common issues or directing users to the appropriate resources.

In conclusion, the inaccessibility of paper (2506.06093)v1 on arXiv serves as a critical reminder of the technical complexities and user interface challenges inherent in managing large-scale academic repositories. Addressing these concerns through robust technological solutions and comprehensive support mechanisms is essential for the continued advancement and dissemination of research in the digital age.