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Arxiv
2d
148
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Execution Guided Line-by-Line Code Generation
- Researchers have introduced a new approach, Execution-Guided Classifier-Free Guidance (EG-CFG), for neural code generation that incorporates real-time execution signals into the process.
- This method dynamically integrates execution signals while generating code, offering line-by-line feedback to steer the model towards executable solutions.
- EG-CFG employs a multi-stage process involving beam search, extraction of execution signals by testing program completions, and integrating these signals into the generation process.
- Consistent signals within the same line and updating signals at line boundaries help maintain coherent guidance and syntactic structure.
- The method enables parallelism at the task level, allowing multiple agents to explore different reasoning paths simultaneously and generate diverse candidate solutions collectively.
- Experiments across various coding tasks have shown that EG-CFG outperforms traditional methods, achieving state-of-the-art results across different complexities, including competitive programming tasks.
- The code for EG-CFG is available at: https://github.com/boazlavon/eg_cfg
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