Cover Image

CodeLutra: Boosting LLM Code Generation via Preference-Guided Refinement

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Tao, Leitian, Chen, Xiang, Yu, Tong, Mai, Tung, Rossi, Ryan, Li, Yixuan, Mitra, Saayan
Format:	Preprint
Published:	2024
Subjects:	Computation and Language
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Your Weak LLM is Secretly a Strong Teacher for Alignment
by: Tao, Leitian, et al.
Published: (2024)

Boosting Chart-to-Code Generation in MLLM via Dual Preference-Guided Refinement
by: Zhang, Zhihan, et al.
Published: (2025)

Limited Preference Data? Learning Better Reward Model with Latent Space Synthesis
by: Tao, Leitian, et al.
Published: (2025)

Detecting Ambiguities to Guide Query Rewrite for Robust Conversations in Enterprise AI Assistants
by: Tanjim, Md Mehrab, et al.
Published: (2025)

ThinkRouter: Efficient Reasoning via Routing Thinking between Latent and Discrete Spaces
by: Xu, Xin, et al.
Published: (2026)

CodeBoost: Boosting Code LLMs by Squeezing Knowledge from Code Snippets with RL
by: Wang, Sijie, et al.
Published: (2025)

Mitigating Forgetting Between Supervised and Reinforcement Learning Yields Stronger Reasoners
by: Yuan, Xiangchi, et al.
Published: (2025)

TreeDiff: AST-Guided Code Generation with Diffusion LLMs
by: Zeng, Yiming, et al.
Published: (2025)

An Interactive Paradigm for Deep Research
by: Ai, Lin, et al.
Published: (2026)

OpenCodeInterpreter: Integrating Code Generation with Execution and Refinement
by: Zheng, Tianyu, et al.
Published: (2024)

CodeRefine: A Pipeline for Enhancing LLM-Generated Code Implementations of Research Papers
by: Trofimova, Ekaterina, et al.
Published: (2024)

Learning Code Preference via Synthetic Evolution
by: Liu, Jiawei, et al.
Published: (2024)

BanglaForge: LLM Collaboration with Self-Refinement for Bangla Code Generation
by: Dihan, Mahir Labib, et al.
Published: (2025)

SAND: Boosting LLM Agents with Self-Taught Action Deliberation
by: Xia, Yu, et al.
Published: (2025)

BigCodeArena: Unveiling More Reliable Human Preferences in Code Generation via Execution
by: Zhuo, Terry Yue, et al.
Published: (2025)

Challenges and Future Directions of Data-Centric AI Alignment
by: Yeh, Min-Hsuan, et al.
Published: (2024)

Sketch Then Generate: Providing Incremental User Feedback and Guiding LLM Code Generation through Language-Oriented Code Sketches
by: Zhu-Tian, Chen, et al.
Published: (2024)

RefineCoder: Iterative Improving of Large Language Models via Adaptive Critique Refinement for Code Generation
by: Zhou, Changzhi, et al.
Published: (2025)

CollabCoder: Plan-Code Co-Evolution via Collaborative Decision-Making for Efficient Code Generation
by: Doan, Duy Tung, et al.
Published: (2026)

Cracking the Code: Enhancing Implicit Hate Speech Detection through Coding Classification
by: Wei, Lu, et al.
Published: (2025)

CYCLE: Learning to Self-Refine the Code Generation
by: Ding, Yangruibo, et al.
Published: (2024)

Latent Preference Coding: Aligning Large Language Models via Discrete Latent Codes
by: Gong, Zhuocheng, et al.
Published: (2025)

Compliance-to-Code: Enhancing Financial Compliance Checking via Code Generation
by: Li, Siyuan, et al.
Published: (2025)

Iterative Refinement of Project-Level Code Context for Precise Code Generation with Compiler Feedback
by: Bi, Zhangqian, et al.
Published: (2024)

Token-Level Adversarial Prompt Detection Based on Perplexity Measures and Contextual Information
by: Hu, Zhengmian, et al.
Published: (2023)

LogitsCoder: Towards Efficient Chain-of-Thought Path Search via Logits Preference Decoding for Code Generation
by: Chen, Jizheng, et al.
Published: (2026)

Hallucination Diversity-Aware Active Learning for Text Summarization
by: Xia, Yu, et al.
Published: (2024)

Code Fingerprints: Disentangled Attribution of LLM-Generated Code
by: Guo, Jiaxun, et al.
Published: (2026)

SymCode: A Neurosymbolic Approach to Mathematical Reasoning via Verifiable Code Generation
by: Nezhad, Sina Bagheri, et al.
Published: (2025)

CodeJudge: Evaluating Code Generation with Large Language Models
by: Tong, Weixi, et al.
Published: (2024)

To Code, or Not To Code? Exploring Impact of Code in Pre-training
by: Aryabumi, Viraat, et al.
Published: (2024)

Code-Optimise: Self-Generated Preference Data for Correctness and Efficiency
by: Gee, Leonidas, et al.
Published: (2024)

Exploring LLM Multi-Agents for ICD Coding
by: Li, Rumeng, et al.
Published: (2024)

PerfCodeGen: Improving Performance of LLM Generated Code with Execution Feedback
by: Peng, Yun, et al.
Published: (2024)

A Penalty Goes a Long Way: Measuring Lexical Diversity in Synthetic Texts Under Prompt-Influenced Length Variations
by: Deshpande, Vijeta, et al.
Published: (2025)

Rethinking Code Refinement: Learning to Judge Code Efficiency
by: Seo, Minju, et al.
Published: (2024)

LLMSniffer: Detecting LLM-Generated Code via GraphCodeBERT and Supervised Contrastive Learning
by: Dihan, Mahir Labib, et al.
Published: (2026)

Code-A1: Adversarial Evolving of Code LLM and Test LLM via Reinforcement Learning
by: Wang, Aozhe, et al.
Published: (2026)

RethinkMCTS: Refining Erroneous Thoughts in Monte Carlo Tree Search for Code Generation
by: Li, Qingyao, et al.
Published: (2024)

Python Symbolic Execution with LLM-powered Code Generation
by: Wang, Wenhan, et al.
Published: (2024)