arXiv AI August 18, 2026 By Liang Lu, Yuan Jiang, Christoph Treude, Shuzheng Gao, Jingyu Xiao, Xiaohong Su, Michael R. Lyu

Enhancing the Non-Functional Quality Compliance of LLM-Generated Code through Quality-Aware Preference Learning

Read the original on arXiv AI →

arXiv:2503. 09020v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have been widely adopted in commercial code completion engines, significantly enhancing coding efficiency and productivity.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jun 30

Reward-Free Code Alignment from Pretrained or Fine-Tuned LLM: Unpacking the Trade-offs for Code Generation

arXiv:2606. 28998v1 Announce Type: cross Abstract: Large Language Model (LLM) alignment trains an LLM using preference data to produce outputs that better meet established quality standards.

By Gias Uddin, Sanjeepan Sivapiran

llms reinforcement-learning fine-tuning benchmarks safety

arXiv Machine Learning

Aug 11

Themis: Training Robust Multilingual Code Reward Models for Flexible Multi-Criteria Scoring

arXiv:2605. 00754v4 Announce Type: replace-cross Abstract: Reward models (RMs) have become an indispensable fixture of the language model (LM) post-training playbook, enabling policy alignment and test-time scaling.

By Indraneil Paul, Goran Glava\v{s}, Iryna Gurevych

llms reinforcement-learning benchmarks safety

arXiv AI

Jun 16

FasterPy: An LLM-based Code Execution Efficiency Optimization Framework

arXiv:2512. 22827v2 Announce Type: replace-cross Abstract: Code often suffers from performance bugs.

By Yue Wu, Minghao Han, Ruiyin Li, Peng Liang, Amjed Tahir, Zengyang Li, Qiong Feng, Mojtaba Shahin

llms rag fine-tuning benchmarks

arXiv AI

Jun 8

SWE-IF: Aligning Code Evaluation with Human Preference

arXiv:2510. 07315v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have catalyzed vibe coding, where users leverage LLMs to generate and iteratively refine code through natural language interactions until it passes their vibe check.

By Ming Zhong, Xiang Zhou, Ting-Yun Chang, Qingze Wang, Nan Xu, Xiance Si, Dan Garrette, Shyam Upadhyay, Jeremiah Liu, Jiawei Han, Benoit Schillings, Jiao Sun

llms

arXiv AI

1d ago

Self-Bootstrapping Automated Program Repair: Using LLMs to Generate and Evaluate Synthetic Training Data for Bug Repair

arXiv:2505. 07372v3 Announce Type: replace-cross Abstract: This paper presents a novel methodology for enhancing Automated Program Repair (APR) through synthetic data generation utilizing Large Language Models (LLMs).

By David de-Fitero-Dominguez, Antonio Garcia-Cabot, Eva Garcia-Lopez

llms benchmarks

arXiv AI

1d ago

When Uncertainty Isn't Enough: An Empirical Study of Self-Correction in Code Generation

arXiv:2608. 14659v1 Announce Type: new Abstract: Large language models for code generation often produce incorrect solutions without reliable indicators of failure.

By Pranav Rakasi, Maanas Lalwani, Arnav Srivastava, Arya Palanivel, Tinuade Adeleke, Ruizhe Li, Sean Wu

llms benchmarks

Related stories

Reward-Free Code Alignment from Pretrained or Fine-Tuned LLM: Unpacking the Trade-offs for Code Generation

Themis: Training Robust Multilingual Code Reward Models for Flexible Multi-Criteria Scoring

FasterPy: An LLM-based Code Execution Efficiency Optimization Framework

SWE-IF: Aligning Code Evaluation with Human Preference

Self-Bootstrapping Automated Program Repair: Using LLMs to Generate and Evaluate Synthetic Training Data for Bug Repair

When Uncertainty Isn't Enough: An Empirical Study of Self-Correction in Code Generation