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RLEF: Grounding Code LLMs in Execution Feedback with Reinforcement Learning

ICML 2025 · Large language models › Agents, code and math · approach: Software engineering agents · 6 citations (OpenAlex)

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Proposes RLEF, an end-to-end PPO-based RL method that trains Llama 3.1 Instruct models to use execution feedback over multi-turn code repair, reaching state-of-the-art CodeContests solve rates at small sample budgets.

Problem

Code LLMs struggle to use execution feedback iteratively; independent sampling often beats iterative repair at fixed inference budgets, and prior agentic scaffolds are costly.

Approach

Frame iterative code synthesis as a partially observable MDP where public-test execution feedback is inserted into the dialog, and train the LLM end-to-end with PPO using a binary reward from private tests, with a token-level policy and turn-level value function.

Key results

Measured effects reported in the paper

What was changedWhereMeasuredEffect
Train with RLEFLlama 3.1 8B · CodeContests test, 1@3solve rate: 10.5 -> 16.0Helped
Train with RLEFLlama 3.1 70B · CodeContests test, 1@3solve rate: 27.5 -> 40.1Helped
Train with RLEFLlama 3.1 8B · CodeContests test, 10@100solve rate: 24.8 -> 28.7Helped
Train with RLEFLlama 3.1 70B · CodeContests test, 10@100solve rate: 50.3 -> 54.5Helped
Train with RLEFLlama 3.1 8B · HumanEval+ MT 1@3solve rate: 63.9 -> 69.5Helped
Train with RLEFLlama 3.1 70B · MBPP+ MT 1@3solve rate: 70.2 -> 72.2Helped
Use few-shot prompting (ablation)Llama 3.1 8B Instruct · CodeContests valid, 1@3solve rate: 8.9 -> 8.5Hurt
Use few-shot prompting (ablation)Llama 3.1 8B Instruct · CodeContests test, 1@3solve rate: 10.5 -> 8.5Hurt
Use SFT instead of RLEF (ablation)Llama 3.1 8B Instruct · CodeContests valid, 1@3solve rate: 8.9 -> 10.3Helped
Use SFT instead of RLEF (ablation)Llama 3.1 8B Instruct · CodeContests test, 1@3solve rate: 10.5 -> 10.0No clear effect
Remove public-test execution feedback during training (ablation)Llama 3.1 8B RLEF · CodeContests valid, 1@3solve rate: 17.2 -> 12.2Hurt
Remove public-test execution feedback during training (ablation)Llama 3.1 8B RLEF · CodeContests test, 1@3solve rate: 16.0 -> 10.9Hurt

Surprising findings

Limitations

Experimental setup

Datasets: CodeContests HumanEval+ MBPP+

Benchmarks: CodeContests HumanEval+ MBPP+

Metrics: n@k solve rate pass@1 pass@10 chrF

Baselines: AlphaCode AlphaCodium MapCoder Code Llama 34B + PPO gpt-4o-2024-05-13 SFT Few-Shot

Builds on: PPO AlphaCode AlphaCodium CodeContests

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