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RLTF: Reinforcement Learning from Unit Test Feedback

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arxiv 2307.04349 v2 pith:H4PN3PI2 submitted 2023-07-10 cs.AI cs.CLcs.LG

Jiate Liu , Yiqin Zhu , Kaiwen Xiao , Qiang Fu , Xiao Han , Wei Yang , Deheng Ye This is my paper
classification cs.AIcs.CLcs.LG
keywords codefeedbackrltftestunitlearningreinforcementllms
verification ladder T0 review T1 audit T2 compute T3 formal
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The goal of program synthesis, or code generation, is to generate executable code based on given descriptions. Recently, there has been an increasing number of studies employing reinforcement learning (RL) to improve the performance of large language models (LLMs) for code. However, current representative works either rely solely on offline frameworks, limiting the exploration of new sample spaces, or fall short in the utilization of unit test signals, not accounting for specific error locations within the code. To address these issues, we propose RLTF, i.e., Reinforcement Learning from Unit Test Feedback, a novel online RL framework with unit test feedback of multi-granularity for refining code LLMs. Our approach generates data in real-time during training and simultaneously utilizes fine-grained feedback signals to guide the model towards producing higher-quality code. Extensive experiments show that RLTF achieves state-of-the-art performance on the APPS and the MBPP benchmarks. Our code is available at: https://github.com/Zyq-scut/RLTF.

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Cited by 8 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. An Iterative Test-and-Repair Framework for Competitive Code Generation

    cs.SE 2026-04 unverdicted novelty 7.0 of 10

    FixAudit improves LLM code generation on competitive programming benchmarks by training a shared model for iterative code-aware test generation and repair, achieving 35%+ gains in Pass@1 over baselines on the same 7B model.

  2. Beyond the Need for Speed: Energy-Aware Code Generation via Simulation-Guided Reinforcement Learning

    cs.LG 2026-07 conditional novelty 6.5 of 10

    Simulation-in-the-loop GRPO on energy-labeled C++ pairs yields 12.63% CARET and beats human energy efficiency on 58.4% of valid held-out outputs, while IPC misranks energy on 67.8% of problems.

  3. RLPF: Reinforcement Learning from Performance Feedback for Code Generation

    cs.LG 2026-07 conditional novelty 6.0 of 10

    RLPF's staged performance reward lifts Qwen3-32B on PerfCodeBench from 11.1% to 54.6% correct-and-runnable and from 8.1% to 38.6% relative efficiency.

  4. Beyond Binary: Turning Partial Success into Dense Verifiable Rewards for Reinforcement Learning in Code Generation

    cs.LG 2026-01 conditional novelty 6.0 of 10

    VeRPO turns partial test-suite success into dense, difficulty-weighted rewards for RL code generation, beating GRPO by up to 8.8 pass@1 points at negligible cost.

  5. Efficiency of turbulence

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    The efficiency of turbulence, the fraction of input energy stored in the flow, appears bounded and may saturate in a power-law manner across several turbulent flows.

  6. BitsAI-Fix: LLM-Driven Approach for Automated Lint Error Resolution in Practice

    cs.SE 2025-08 conditional novelty 6.0 of 10

    A 32B LLM trained with progressive reinforcement learning on cold-start and user-feedback data fixes Go lint errors at 84.68% accuracy with 1.72% redundancy on a private ByteDance test set.

  7. Dr. Boot: Bootstrapping Program Synthesis Language Models to Perform Repairing

    cs.SE 2025-07 conditional novelty 6.0 of 10

    A bootstrapping algorithm that trains program synthesis models on their own successful outputs and on repaired failures modestly improves pass@k over regular fine-tuning on MBPP, with mixed results on APPS.

  8. InfoSynth: Information-Guided Benchmark Synthesis for LLMs

    cs.CL 2026-01 conditional novelty 5.0 of 10

    Using KL divergence and entropy on embeddings, InfoSynth scores benchmark novelty/diversity and guides a genetic pipeline that generates new, code-verified Python problems from seeds.

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