RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learning

• URL: http://arxiv.org/abs/2410.02089v1
• Date: Wed, 2 Oct 2024 23:25:17 GMT
• Title: RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learning
• Authors: Jonas Gehring, Kunhao Zheng, Jade Copet, Vegard Mella, Taco Cohen, Gabriel Synnaeve,
• Abstract summary: Large language models (LLMs) deployed as agents solve user-specified tasks over multiple steps while keeping the required manual engagement to a minimum. We propose an end-to-end reinforcement learning method for teaching models to leverage execution feedback in the realm of code synthesis.
• Score: 35.446870721902904
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) deployed as agents solve user-specified tasks over multiple steps while keeping the required manual engagement to a minimum. Crucially, such LLMs need to ground their generations in any feedback obtained to reliably achieve desired outcomes. We propose an end-to-end reinforcement learning method for teaching models to leverage execution feedback in the realm of code synthesis, where state-of-the-art LLMs struggle to improve code iteratively compared to independent sampling. We benchmark on competitive programming tasks, where we achieve new start-of-the art results with both small (8B parameters) and large (70B) models while reducing the amount of samples required by an order of magnitude. Our analysis of inference-time behavior demonstrates that our method produces LLMs that effectively leverage automatic feedback over multiple steps.

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