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CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning

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arxiv 2207.01780 v3 pith:V325N4CR submitted 2022-07-05 cs.LG cs.CLcs.PL

Hung Le , Yue Wang , Akhilesh Deepak Gotmare , Silvio Savarese , Steven C.H. Hoi This is my paper
classification cs.LGcs.CLcs.PL
keywords generationmodelresultscodelearningpretrainedproblemprogram
verification ladder T0 review T1 audit T2 compute T3 formal
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Program synthesis or code generation aims to generate a program that satisfies a problem specification. Recent approaches using large-scale pretrained language models (LMs) have shown promising results, yet they have some critical limitations. In particular, they often follow a standard supervised fine-tuning procedure to train a code generation model only from the pairs of natural-language problem descriptions and ground-truth programs. Such paradigm largely ignores some important but potentially useful signals in the problem specification such as unit tests, which thus often results in poor performance when solving complex unseen coding tasks. To address the limitations, we propose "CodeRL", a new framework for program synthesis tasks through pretrained LMs and deep reinforcement learning (RL). Specifically, during training, we treat the code-generating LM as an actor network, and introduce a critic network that is trained to predict the functional correctness of generated programs and provide dense feedback signals to the actor. During inference, we introduce a new generation procedure with a critical sampling strategy that allows a model to automatically regenerate programs based on feedback from example unit tests and critic scores. For the model backbones, we extended the encoder-decoder architecture of CodeT5 with enhanced learning objectives, larger model sizes, and better pretraining data. Our method not only achieves new SOTA results on the challenging APPS benchmark, but also shows strong zero-shot transfer capability with new SOTA results on the simpler MBPP benchmark.

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Cited by 9 Pith papers

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

  1. Self-Improving Large Language Models via Progressive Experience Evolution

    cs.CL 2026-08 conditional novelty 7.0 of 10

    SPEE improves LLM math reasoning by explicitly evolving a global pool of transferable experience and distilling it into the policy before applying GRPO reinforcement learning.

  2. Embarrassingly Simple Self-Distillation Improves Code Generation

    cs.CL 2026-04 conditional novelty 7.0 of 10

    Simple self-distillation—fine-tuning a code model on its own temperature-sampled, truncated outputs—raises LiveCodeBench pass@1 substantially without verifiers, teachers, or RL.

  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. NKI-Agent: Domain-Specific Fine-Tuning and Agentic Tool Use for Neuron Kernel Generation

    cs.LG 2026-07 conditional novelty 6.0 of 10

    An agent with compile/verify tools reaches 77.3% NKI kernel pass rate on real Trn1 hardware with Opus 4.8, versus 6% single-shot; SFT Qwen3-30B hits 25% at ~1/100th cost, and binary-reward GRPO fails to beat SFT.

  5. Re:Form -- Reducing Human Annotations in Scalable Formal Software Verification with RL in LLMs: A Preliminary Study on Dafny

    cs.CL 2025-07 conditional novelty 6.0 of 10

    A minimal-prior pipeline with automated data curation and verifier-driven RL lets small LLMs generate verifiable Dafny specifications and beat larger proprietary models on a synthetic compositional benchmark.

  6. 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.

  7. Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation

    cs.SE 2025-07 reject novelty 6.0 of 10

    An empirical study of 1,023 CoT-code pairs shows that 76.4% of LLM-generated CoTs are low quality and that CoT correctness does not guarantee code correctness.

  8. AutoTriton: Automatic Triton Programming with Reinforcement Learning in LLMs

    cs.LG 2025-07 conditional novelty 6.0 of 10

    An 8B model trained with SFT and GRPO reinforcement learning generates Triton kernels that match or exceed frontier model performance on TritonBench and KernelBench correctness metrics.

  9. Accelerating Reinforcement Learning Algorithms Convergence using Pre-trained Large Language Models as Tutors With Advice Reusing

    cs.LG 2025-09 conditional novelty 4.0 of 10

    LLM tutoring modestly accelerates RL convergence on average, with advice reuse saving wall-clock time but reducing stability.

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