QiMeng-PRepair: Precise Code Repair via Edit-Aware Reward Optimization

Changxin Ke; Di Huang; Jiaming Guo; Li Ding; Qi Guo; Rui Zhang; Shuo Wang; Xing Hu; Xiong Peng; Xuyuan Zhu

arxiv: 2604.05963 · v1 · submitted 2026-04-07 · 💻 cs.SE · cs.LG

QiMeng-PRepair: Precise Code Repair via Edit-Aware Reward Optimization

Changxin Ke , Rui Zhang , Jiaming Guo , Yuanbo Wen , Li Ding , Shuo Wang , Xuyuan Zhu , Xiong Peng

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Di Huang Zidong Du Xing Hu Qi Guo Yunji Chen

This is my paper

Pith reviewed 2026-05-10 18:37 UTC · model grok-4.3

classification 💻 cs.SE cs.LG

keywords code repairprogram repairlarge language modelsreinforcement learningpolicy optimizationover-editingbug fixingself-training

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The pith

Language models learn precise code repairs by training on self-generated bugs with rewards that penalize unnecessary edits.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Large language models often over-edit when repairing code, rewriting correct sections and complicating bug fixes. The paper defines a precise repair task that requires maximizing reuse of correct code while changing only the buggy parts. PRepair implements this via Self-Breaking, which creates varied buggy programs through controlled injection and sampling, and Self-Repairing, which applies edit-aware group relative policy optimization to reward minimal correct changes. Experiments demonstrate that the resulting models achieve higher repair precision on a metric combining correctness and edit extent. The framework also supports faster inference when paired with speculative editing.

Core claim

PRepair mitigates over-editing in LLM-based program repair by training on diverse self-generated bugs and optimizing with an edit-aware reward that encourages only the necessary modifications, thereby maximizing reuse of correct code and improving overall repair accuracy.

What carries the argument

Edit-Aware Group Relative Policy Optimization (EA-GRPO), which augments standard policy optimization with a reward signal based on the extent and correctness of code edits to favor minimal yet complete fixes.

Load-bearing premise

Self-generated buggy examples combined with an edit-aware reward will reliably steer the model toward minimal correct repairs without missing bugs or needing human repair labels.

What would settle it

On a held-out test set, models trained with PRepair produce either more edits than necessary or fail to correct the injected bugs at rates comparable to or worse than baseline fine-tuning.

Figures

Figures reproduced from arXiv: 2604.05963 by Changxin Ke, Di Huang, Jiaming Guo, Li Ding, Qi Guo, Rui Zhang, Shuo Wang, Xing Hu, Xiong Peng, Xuyuan Zhu, Yuanbo Wen, Yunji Chen, Zidong Du.

**Figure 1.** Figure 1: Existing models suffer from over-editing, which not only reduces repair accuracy but also significantly increases the review burden for developers. In comparison, PRepair improves both repair accuracy and maintainability in practice. train models capable of performing program repair accurately. Most existing training approaches (Muennighoff et al., 2023; Hui et al., 2024; Yang et al., 2025; Fu et al., 202… view at source ↗

**Figure 2.** Figure 2: GRPO training with correctness-only rewards. For both Python and Verilog code repair tasks, although performance improves during training, the edit cost increases substantially, leading to a more severe over-editing phenomenon. Here, dividing |X| normalizes the edit distance by lines of buggy code, allowing fair comparison across programs of different sizes. 2.1 Observations In this section, we explore th… view at source ↗

**Figure 3.** Figure 3: Overview of the PRepair framework. It consists of two stages: Self-Breaking, where the model injects diverse bugs into golden programs to construct high-quality buggy inputs, and Self-Repairing, where the model learns to precise repair these buggy programs via EA-GRPO which uses a dynamic edit-aware reward to encourage minimal yet correct edits. Specifically, we select a subset Xs ⊂ X by minimizing the ma… view at source ↗

**Figure 4.** Figure 4: Code repair performance of in-domain and cross-domain. We plot the changes of pass@1 and fix1@1 across training steps, reporting both in-domain and cross-domain performance. (a) In-domain: models are trained on Python data and evaluated on Python code repair; similarly for Verilog. (b) Cross-domain: models trained on Python data are evaluated on Verilog code repair, and vice versa. 15% 6% 35% 5% [PITH_FUL… view at source ↗

**Figure 5.** Figure 5: Decoding Performance with Speculative Edits. Throughput and acceptance rates of Origin (before training), GRPO, and EA-GRPO on Python and Verilog benchmarks, using buggy code as draft. Fewer Edits, More Correct Repairs. As shown in [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗

**Figure 6.** Figure 6: Comparison of attention scores in code repair. The top figure shows the PRepair model trained with EA-GRPO, and the bottom figure shows the model trained with GRPO using correctness-only rewards. The vertical axis corresponds to output tokens, the horizontal axis corresponds to input tokens, and the color intensity indicates the relative magnitude of the attention score. W = (Mout) ⊤ A Min Here, Wij repres… view at source ↗

read the original abstract

Large Language Models (LLMs) achieve strong program repair performance but often suffer from over-editing, where excessive modifications overwrite correct code and hinder bug localization. We systematically quantify its impact and introduce precise repair task, which maximizes reuse of correct code while fixing only buggy parts. Building on this insight, we propose PRepair, a framework that mitigates over-editing and improves repair accuracy. PRepair has two components: Self-Breaking, which generates diverse buggy programs via controlled bug injection and min-max sampling, and Self-Repairing, which trains models with Edit-Aware Group Relative Policy Optimization (EA-GRPO) using an edit-aware reward to encourage minimal yet correct edits. Experiments show that PRepair improves repair precision by up to 31.4% under $\mathrm{fix}_1@1$, a metric that jointly considers repair correctness and extent, and significantly increases decoding throughput when combined with speculative editing, demonstrating its potential for precise and practical code repair.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

PRepair formalizes minimal-edit code repair and adds an edit-aware RL stage that lifts precision on their fix₁@1 metric.

read the letter

The core contribution is a clear definition of precise repair—maximizing reuse of correct code while fixing only the buggy parts—plus a two-stage method to train for it. Self-Breaking creates synthetic bugs through controlled injection and min-max sampling; Self-Repairing then applies EA-GRPO, their edit-aware variant of group relative policy optimization, with a reward that explicitly trades off edit size against functional correctness. They report gains up to 31.4% on fix₁@1, which scores both correctness and minimal change, and note throughput benefits when paired with speculative decoding. That framing and the reward design are the genuinely new pieces; prior RL-for-repair work did not tie the objective this directly to edit extent. The self-supervised loop is also practical, since it reduces reliance on human-labeled fixes. The experiments appear to test the combination on standard repair benchmarks and show consistent lifts, which is useful evidence for the claim. The main soft spot is that the abstract gives little detail on baseline selection, statistical significance, or how they quantified over-editing on the test set, so the size of the real-world advantage is still hard to judge without the full tables and controls. Synthetic bug generation could also favor the method if the injected faults are easier to localize than natural ones, though the min-max sampling tries to mitigate that. This is solid work for the LLM-for-code community. Readers building repair tools or studying reward design in program synthesis will get concrete ideas they can try. The method is described at a level that supports reproduction, and the empirical results are falsifiable, so it deserves a serious referee rather than a desk reject.

Referee Report

2 major / 3 minor

Summary. The paper claims that LLMs for program repair suffer from over-editing, which the authors quantify and address by defining a 'precise repair' task that maximizes reuse of correct code. They introduce PRepair, consisting of Self-Breaking (controlled bug injection with min-max sampling to generate diverse buggy programs) and Self-Repairing (training via Edit-Aware Group Relative Policy Optimization (EA-GRPO) with an edit-aware reward). Experiments report up to 31.4% gains in the fix₁@1 metric (which jointly scores correctness and edit extent) plus improved decoding throughput via speculative editing.

Significance. If the reported precision gains and the reliability of the edit-aware reward hold under rigorous controls, the work could meaningfully advance practical LLM-based repair by reducing unnecessary changes to correct code. The fix₁@1 metric and the synthetic bug-generation approach are potentially useful contributions for evaluating and training minimal-edit repair models.

major comments (2)

[§4, Table 2] §4 (Experiments), Table 2 and the fix₁@1 definition: the abstract and results claim a 31.4% improvement, but the manuscript must explicitly state the exact formula for fix₁@1, the full list of baselines (including whether they use the same base model and decoding settings), dataset sizes, and statistical significance tests; without these, the central empirical claim cannot be verified as load-bearing.
[§3.2] §3.2 (EA-GRPO): the edit-aware reward is described as trading off minimality against correctness, but the manuscript should provide the precise reward function (including any coefficients) and an ablation showing that removing the edit term collapses performance; otherwise the claim that EA-GRPO reliably prevents over-editing rests on an untested assumption.

minor comments (3)

[§1] The abstract and §1 should cite prior work on over-editing in code repair (e.g., recent studies on LLM repair precision) to better situate the contribution.
[Figure 1] Figure 1 (overview) and the Self-Breaking description would benefit from a small example showing a concrete bug injection and the resulting min-max sample pair.
[§3.1] The paper should release the synthetic bug-generation code and the exact prompts used for Self-Breaking to support reproducibility.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which help strengthen the verifiability of our empirical results. We address each major comment below and will revise the manuscript to incorporate the requested clarifications.

read point-by-point responses

Referee: [§4, Table 2] §4 (Experiments), Table 2 and the fix₁@1 definition: the abstract and results claim a 31.4% improvement, but the manuscript must explicitly state the exact formula for fix₁@1, the full list of baselines (including whether they use the same base model and decoding settings), dataset sizes, and statistical significance tests; without these, the central empirical claim cannot be verified as load-bearing.

Authors: We agree that these details are necessary for full verification. In the revised manuscript we will: (1) state the exact formula for fix₁@1 in §4, (2) provide the complete list of baselines together with their base models and decoding settings, (3) report the precise dataset sizes used in each experiment, and (4) add statistical significance tests (bootstrap confidence intervals and paired t-tests) supporting the reported gains. revision: yes
Referee: [§3.2] §3.2 (EA-GRPO): the edit-aware reward is described as trading off minimality against correctness, but the manuscript should provide the precise reward function (including any coefficients) and an ablation showing that removing the edit term collapses performance; otherwise the claim that EA-GRPO reliably prevents over-editing rests on an untested assumption.

Authors: We will insert the precise mathematical definition of the edit-aware reward (including all weighting coefficients) into §3.2. We will also add an ablation experiment that removes the edit term from the reward and reports the resulting drop in fix₁@1, thereby confirming the contribution of the edit-aware component. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical training/evaluation framework

full rationale

The paper presents an empirical framework (Self-Breaking for synthetic bug generation + EA-GRPO training with edit-aware reward) evaluated on repair precision metrics such as fix₁@1. No derivation chain, mathematical model, or uniqueness theorem is claimed; the central result is an observed performance gain from the proposed training procedure. All load-bearing elements are externally falsifiable via standard ML benchmarks and do not reduce to self-defined fitted quantities or self-citation chains. This matches the expected non-circular outcome for a purely empirical methods paper.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

Only the abstract is available, so specific free parameters, axioms, and entities cannot be audited in detail; the approach implicitly relies on standard RL assumptions for LLM fine-tuning.

free parameters (1)

edit-aware reward coefficients
Weights balancing minimal edit size against correctness are likely tuned but not specified.

axioms (1)

domain assumption Controlled synthetic bug injection produces training signals representative of real-world bugs
Invoked by the Self-Breaking component to generate diverse buggy programs.

pith-pipeline@v0.9.0 · 5504 in / 1110 out tokens · 33354 ms · 2026-05-10T18:37:29.253105+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

5 extracted references · 5 canonical work pages · 1 internal anchor

[1]

Evaluating Large Language Models Trained on Code

Evaluating large language models trained on code.arXiv preprint arXiv:2107.03374. Will Epperson, Gagan Bansal, Victor C Dibia, Adam Fourney, Jack Gerrits, Erkang (Eric) Zhu, and Saleema Amershi. 2025. Interactive debugging and steering of multi-agent ai systems. InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems, CHI ’25, page ...

work page internal anchor Pith review Pith/arXiv arXiv 2025
[2]

Jiale Guo, Suizhi Huang, Mei Li, Dong Huang, Xing- sheng Chen, Regina Zhang, Zhijiang Guo, Han Yu, Siu-Ming Yiu, Pietro Lio, and Kwok-Yan Lam

Slmfix: Leveraging small language models for error fixing with reinforcement learning.Preprint, arXiv:2511.19422. Jiale Guo, Suizhi Huang, Mei Li, Dong Huang, Xing- sheng Chen, Regina Zhang, Zhijiang Guo, Han Yu, Siu-Ming Yiu, Pietro Lio, and Kwok-Yan Lam. 2025. A comprehensive survey on benchmarks and solu- tions in software engineering of llm-empowered ...

work page arXiv 2025
[3]

Verilogcoder: Autonomous verilog coding agents with graph-based planning and abstract syn- tax tree (ast)-based waveform tracing tool.Preprint, arXiv:2408.08927. Binyuan Hui, Jian Yang, Zeyu Cui, Jiaxi Yang, Day- iheng Liu, Lei Zhang, Tianyu Liu, Jiajun Zhang, Bowen Yu, Keming Lu, Kai Dang, Yang Fan, Yichang Zhang, An Yang, Rui Men, Fei Huang, Bo Zheng, Y...

work page arXiv 2024
[4]

Leetcodedataset: A temporal dataset for robust evaluation and efficient training of code llms

Leetcodedataset: A temporal dataset for ro- bust evaluation and efficient training of code llms. Preprint, arXiv:2504.14655. Junjielong Xu, Ying Fu, Shin Hwei Tan, and Pinjia He

work page arXiv
[5]

round away from zero

Aligning the objective of llm-based program repair.Preprint, arXiv:2404.08877. Boyang Yang, Haoye Tian, Jiadong Ren, Hongyu Zhang, Jacques Klein, Tegawende Bissyande, Claire Le Goues, and Shunfu Jin. 2025. Morepair: Teaching llms to repair code via multi-objective fine-tuning. ACM Transactions on Software Engineering and Methodology. Xufeng Yao, Haoyang L...

work page arXiv 2025

[1] [1]

Evaluating Large Language Models Trained on Code

Evaluating large language models trained on code.arXiv preprint arXiv:2107.03374. Will Epperson, Gagan Bansal, Victor C Dibia, Adam Fourney, Jack Gerrits, Erkang (Eric) Zhu, and Saleema Amershi. 2025. Interactive debugging and steering of multi-agent ai systems. InProceedings of the 2025 CHI Conference on Human Factors in Computing Systems, CHI ’25, page ...

work page internal anchor Pith review Pith/arXiv arXiv 2025

[2] [2]

Jiale Guo, Suizhi Huang, Mei Li, Dong Huang, Xing- sheng Chen, Regina Zhang, Zhijiang Guo, Han Yu, Siu-Ming Yiu, Pietro Lio, and Kwok-Yan Lam

Slmfix: Leveraging small language models for error fixing with reinforcement learning.Preprint, arXiv:2511.19422. Jiale Guo, Suizhi Huang, Mei Li, Dong Huang, Xing- sheng Chen, Regina Zhang, Zhijiang Guo, Han Yu, Siu-Ming Yiu, Pietro Lio, and Kwok-Yan Lam. 2025. A comprehensive survey on benchmarks and solu- tions in software engineering of llm-empowered ...

work page arXiv 2025

[3] [3]

Verilogcoder: Autonomous verilog coding agents with graph-based planning and abstract syn- tax tree (ast)-based waveform tracing tool.Preprint, arXiv:2408.08927. Binyuan Hui, Jian Yang, Zeyu Cui, Jiaxi Yang, Day- iheng Liu, Lei Zhang, Tianyu Liu, Jiajun Zhang, Bowen Yu, Keming Lu, Kai Dang, Yang Fan, Yichang Zhang, An Yang, Rui Men, Fei Huang, Bo Zheng, Y...

work page arXiv 2024

[4] [4]

Leetcodedataset: A temporal dataset for robust evaluation and efficient training of code llms

Leetcodedataset: A temporal dataset for ro- bust evaluation and efficient training of code llms. Preprint, arXiv:2504.14655. Junjielong Xu, Ying Fu, Shin Hwei Tan, and Pinjia He

work page arXiv

[5] [5]

round away from zero

Aligning the objective of llm-based program repair.Preprint, arXiv:2404.08877. Boyang Yang, Haoye Tian, Jiadong Ren, Hongyu Zhang, Jacques Klein, Tegawende Bissyande, Claire Le Goues, and Shunfu Jin. 2025. Morepair: Teaching llms to repair code via multi-objective fine-tuning. ACM Transactions on Software Engineering and Methodology. Xufeng Yao, Haoyang L...

work page arXiv 2025