Image de couverture de livre

CYCLE: Learning to Self-Refine the Code Generation

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Ding, Yangruibo, Min, Marcus J., Kaiser, Gail, Ray, Baishakhi
Format:	Preprint
Publié:	2024
Sujets:	Software Engineering Computation and Language
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

SemCoder: Training Code Language Models with Comprehensive Semantics Reasoning
par: Ding, Yangruibo, et autres
Publié: (2024)

Beyond Accuracy: Evaluating Self-Consistency of Code Large Language Models with IdentityChain
par: Min, Marcus J., et autres
Publié: (2023)

Automated Code Editing with Search-Generate-Modify
par: Liu, Changshu, et autres
Publié: (2023)

Vulnerability Detection with Code Language Models: How Far Are We?
par: Ding, Yangruibo, et autres
Publié: (2024)

SWE-Spot: Building Small Repo-Experts with Repository-Centric Learning
par: Peng, Jinjun, et autres
Publié: (2026)

Dynamic Benchmarking of Reasoning Capabilities in Code Large Language Models Under Data Contamination
par: Chen, Simin, et autres
Publié: (2025)

Can LLM Prompting Serve as a Proxy for Static Analysis in Vulnerability Detection
par: Ceka, Ira, et autres
Publié: (2024)

CWEval: Outcome-driven Evaluation on Functionality and Security of LLM Code Generation
par: Peng, Jinjun, et autres
Publié: (2025)

BanglaForge: LLM Collaboration with Self-Refinement for Bangla Code Generation
par: Dihan, Mahir Labib, et autres
Publié: (2025)

LeDex: Training LLMs to Better Self-Debug and Explain Code
par: Jiang, Nan, et autres
Publié: (2024)

Code Reasoning for Software Engineering Tasks: A Survey and A Call to Action
par: Pujar, Saurabh, et autres
Publié: (2025)

Iterative Refinement of Project-Level Code Context for Precise Code Generation with Compiler Feedback
par: Bi, Zhangqian, et autres
Publié: (2024)

REINFOREST: Reinforcing Semantic Code Similarity for Cross-Lingual Code Search Models
par: Saieva, Anthony, et autres
Publié: (2023)

SelfCodeAlign: Self-Alignment for Code Generation
par: Wei, Yuxiang, et autres
Publié: (2024)

CodeSense: a Real-World Benchmark and Dataset for Code Semantic Reasoning
par: Roy, Monoshi Kumar, et autres
Publié: (2025)

RethinkMCTS: Refining Erroneous Thoughts in Monte Carlo Tree Search for Code Generation
par: Li, Qingyao, et autres
Publié: (2024)

SpecTra: Enhancing the Code Translation Ability of Language Models by Generating Multi-Modal Specifications
par: Nitin, Vikram, et autres
Publié: (2024)

Rethinking Code Refinement: Learning to Judge Code Efficiency
par: Seo, Minju, et autres
Publié: (2024)

OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement
par: Zheng, Tianyu, et autres
Publié: (2024)

ReflexiCoder: Teaching Large Language Models to Self-Reflect on Generated Code and Self-Correct It via Reinforcement Learning
par: Jiang, Juyong, et autres
Publié: (2026)

EffiLearner: Enhancing Efficiency of Generated Code via Self-Optimization
par: Huang, Dong, et autres
Publié: (2024)

Enhancing Code LLMs with Reinforcement Learning in Code Generation: A Survey
par: Wang, Junqiao, et autres
Publié: (2024)

Generating Equivalent Representations of Code By A Self-Reflection Approach
par: Li, Jia, et autres
Publié: (2024)

LLMSniffer: Detecting LLM-Generated Code via GraphCodeBERT and Supervised Contrastive Learning
par: Dihan, Mahir Labib, et autres
Publié: (2026)

SRLCG: Self-Rectified Large-Scale Code Generation with Multidimensional Chain-of-Thought and Dynamic Backtracking
par: Ma, Hongru, et autres
Publié: (2025)

LLM-as-a-Judge for Reference-less Automatic Code Validation and Refinement for Natural Language to Bash in IT Automation
par: Vo, Ngoc Phuoc An, et autres
Publié: (2025)

Evaluation of Code LLMs on Geospatial Code Generation
par: Gramacki, Piotr, et autres
Publié: (2024)

HumanEval Pro and MBPP Pro: Evaluating Large Language Models on Self-invoking Code Generation
par: Yu, Zhaojian, et autres
Publié: (2024)

Deployability-Centric Infrastructure-as-Code Generation: Fail, Learn, Refine, and Succeed through LLM-Empowered DevOps Simulation
par: Zhang, Tianyi, et autres
Publié: (2025)

VersiCode: Towards Version-controllable Code Generation
par: Wu, Tongtong, et autres
Publié: (2024)

Code Fingerprints: Disentangled Attribution of LLM-Generated Code
par: Guo, Jiaxun, et autres
Publié: (2026)

DevEval: A Manually-Annotated Code Generation Benchmark Aligned with Real-World Code Repositories
par: Li, Jia, et autres
Publié: (2024)

StepCoder: Improve Code Generation with Reinforcement Learning from Compiler Feedback
par: Dou, Shihan, et autres
Publié: (2024)

CodeRAG-Bench: Can Retrieval Augment Code Generation?
par: Wang, Zora Zhiruo, et autres
Publié: (2024)

Is Your AI-Generated Code Really Safe? Evaluating Large Language Models on Secure Code Generation with CodeSecEval
par: Wang, Jiexin, et autres
Publié: (2024)

SWE-Pruner: Self-Adaptive Context Pruning for Coding Agents
par: Wang, Yuhang, et autres
Publié: (2026)

Improving Small Language Models for Code Generation with Reinforcement Learning from Verification Feedback
par: Skopin, Egor, et autres
Publié: (2026)

EvoCodeBench: An Evolving Code Generation Benchmark with Domain-Specific Evaluations
par: Li, Jia, et autres
Publié: (2024)

CodeBenchGen: Creating Scalable Execution-based Code Generation Benchmarks
par: Xie, Yiqing, et autres
Publié: (2024)

AutoCodeBench: Large Language Models are Automatic Code Benchmark Generators
par: Chou, Jason, et autres
Publié: (2025)