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RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learning

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arxiv 2410.02089 v2 pith:ZMMNP36S submitted 2024-10-02 cs.CL cs.AI

Jonas Gehring , Kunhao Zheng , Jade Copet , Vegard Mella , Quentin Carbonneaux , Taco Cohen , Gabriel Synnaeve This is my paper · ORCID
classification cs.CLcs.AI
keywords llmsfeedbackcodemodelsachieveexecutionlargelearning
verification ladder T0 review T1 audit T2 compute T3 formal
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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 the 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 state-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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Cited by 22 Pith papers

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