5 papers · 1 filter
From Code Smells to Best Practices: Tackling Resource Leaks in PyTorch, TensorFlow, and Keras
Bashar Abdallah, Martyna E. Wojciechowska, Gustavo Santos +4
Much of the existing ML research focuses on model performance metrics, leaving limited attention to the long-term sustainability and resource efficiency of ML applications. While h…
RL Fine-Tuning Heals OOD Forgetting in SFT
Hangzhan Jin, Sitao Luan, Tianwei Ni +5
Supervised Fine-Tuning (SFT) followed by Reinforcement Learning (RL) is a standard post-training recipe for improving Large Language Models (LLM) reasoning, but why it works remain…
RL Is Neither a Panacea Nor a Mirage: Understanding Supervised vs. Reinforcement Learning Fine-Tuning for LLMs
Hangzhan Jin, Sicheng Lv, Sifan Wu +1
Training large language models (LLMs) from scratch is increasingly impractical, making post-training methods such as supervised fine-tuning (SFT) and reinforcement-learning fine-tu…
CCCI: Code Completion with Contextual Information for Complex Data Transfer Tasks Using Large Language Models
Hangzhan Jin, Mohammad Hamdaqa
Unlike code generation, which involves creating code from scratch, code completion focuses on integrating new lines or blocks of code into an existing codebase. This process requir…
Smells-sus: Sustainability Smells in IaC
Seif Kosbar, Mohammad Hamdaqa
Practitioners use Infrastructure as Code (IaC) scripts to efficiently configure IT infrastructures through machine-readable definition files. However, during the development of the…