Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs (2025.naacl-srw)
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| Challenge: | Code-generating Large Language Models (LLMs) have become essential tools in modern software development, enhancing productivity and accelerating development. |
| Approach: | They propose to use Reinforcement Learning and Direct Preference Optimization to fine-tune code-generating Large Language Models (LLMs) by enhancing the training data with symbolic execution techniques. |
| Outcome: | The proposed model improves on the CodeRL benchmark and shows that it is more accurate and objective than the baseline model. |
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