10 papers
Performance, Efficiency and Collapse -- Advantages and Challenges in Offline Post-training of Code LLMs
Abhinav Anand, Sanjana Reddy Pachika, Shweta Verma +1
Post-training with reinforcement learning (RL) is a critical phase in the development of code-generating large language models (LLMs), as it ensures adherence to instructions and t…
SHERLOC: Structured Diagnostic Localization for Code Repair Agents
Hovhannes Tamoyan, Sean Narenthiran, Erik Arakelyan +2
LLM agents solve repository-level coding tasks through multi-turn tool use, but utilize half their budget on locating faults before editing. Dedicated localization frameworks have…
Efficient Post-training of LLMs for Code Generation With Offline Reinforcement Learning
Mingze Wu, Abhinav Anand, Shweta Verma +1
Post-training using online reinforcement learning (RL) is an important training step for LLMs, including code-generating models. However, online RL for code generation involves LLM…
Domain-Adaptable Reinforcement Learning for Code Generation with Dense Rewards
Erfan Aghadavoodi Jolfaei, Daniel Maninger, Abhinav Anand +2
Large language models show strong potential for automated code generation, but lack guarantees for correctness, quality, safety, and domain-specific constraints. For instance in ro…
Beyond BLEU: A Semantic Evaluation Method for Code Translation
Julius Näumann, Sven Keidel, Amir Molzam Sharifloo +1
Code translation is one of the core capabilities of LLMs. However, evaluating the correctness of translations remains difficult, as commonly used metrics such as BLEU measure only…
Deep Graph-Language Fusion for Structure-Aware Code Generation
Mert Tiftikci, Amir Molzam Sharifloo, Mira Mezini
Pre-trained Language Models (PLMs) have the potential to transform software development tasks. However, despite significant advances, current PLMs struggle to capture the structure…