arXiv Machine Learning

Beyond the Need for Speed: Energy-Aware Code Generation via Simulation-Guided Reinforcement Learning

arXiv:2607. 04577v1 Announce Type: new Abstract: Code models strictly prioritize functional correctness, leaving software energy efficiency as an unoptimized byproduct.

Hugging Face Trending Papers

Jul 6

Beyond the Need for Speed: Energy-Aware Code Generation via Simulation-Guided Reinforcement Learning

Code models strictly prioritize functional correctness, leaving software energy efficiency as an unoptimized byproduct. Training models to generate energy-efficient code requires reproducible feedback at scale, which physical hardware measurement cannot reliably provide due to variance.

reinforcement-learning fine-tuning efficiency

arXiv AI

22h ago

Beyond FLOPs: Energy-Aware Knowledge Distillation for Sustainable LLMs on Code-Related Task

arXiv:2608. 17515v1 Announce Type: cross Abstract: Background: Large Language Models (LLMs) are increasingly being applied to Software Engineering (SE) tasks, achieving high accuracy across problems such as clone detection, vulnerability prediction, and code summarization.

By Enrique Barba Roque, Lu\'is Cruz, Annibale Panichella

llms nlp efficiency

arXiv AI

Jun 3

Aletheia: What Makes RLVR For Code Verifiers Tick?

arXiv:2601. 12186v3 Announce Type: replace-cross Abstract: Multi-domain thinking verifiers trained via Reinforcement Learning with Verifiable Rewards (RLVR) are a cornerstone of modern post-training.

By Vatsal Venkatkrishna, Indraneil Paul, Iryna Gurevych

reinforcement-learning

arXiv Machine Learning

Aug 4

LEAP: Lean Environment-Feedback via Adaptive Pruning for Code RL in GPU Kernel Generation

arXiv:2608. 01804v1 Announce Type: new Abstract: Post-training large language models (LLMs) via reinforcement learning (RL) has significantly advanced code generation capabilities.

By Tankun Li, Zhi Chen, Yaohua Tang

llms reinforcement-learning efficiency benchmarks safety

arXiv Machine Learning

Jul 10

Selective Left-Shift: Turning Test-Time Compute and Difficulty-based Curation into Training Data for Low-Resource Code Generation

arXiv:2607. 07748v1 Announce Type: new Abstract: Large Language Models achieve strong code generation for high resource languages like Python and Java but suffer sharp performance drops on Low-Resource Programming Languages~(LRPLs) such as Julia.

By Didula Samaraweera, Anjana Supun, Srinath Perera

llms reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 18

Breaking the Solver Bottleneck: Training Task Generators at the Learnable Frontier

arXiv:2606. 18284v1 Announce Type: cross Abstract: The limiting resource for training agents via reinforcement learning (RL) is increasingly frontier task supply: valid, solvable tasks just difficult enough to train the current model.

By Lorenz Wolf, Connor Watts, Roger Creus Castanyer, Geoffrey Bradway, Maxwill Lin, Augustine N. Mavor-Parker, Matthew Daborn-Sargent

agents reinforcement-learning

arXiv Machine Learning

Aug 7

Hybrid-Adaptive Thread Tuning to Mitigate Simulation Execution Bottlenecks in High-Performance Reinforcement Learning Inference

arXiv:2608. 06025v1 Announce Type: new Abstract: In simulation-in-the-loop decision-making systems, reinforcement learning (RL) inference is often constrained by simulator-side execution overhead, where workloads are highly dynamic and sensitive to runtime thread configurations.

By Jiming Su, Hantao Hua, Lujia Yin, Yiping Yao, Feng Zhu

reinforcement-learning fine-tuning efficiency benchmarks

arXiv AI

Jul 13

Attention to Detail: Evaluating Energy, Performance, and Accuracy Trade-offs Across vLLM Configurations

arXiv:2607. 09172v1 Announce Type: cross Abstract: Large Language Models are reshaping how software is developed and maintained.

By Nada Zine, Tristan Coignion, Vincenzo Stoico, Cl\'ement Quinton, Romain Rouvoy, Patricia Lago

llms

arXiv AI

Jul 22

SciCodePile: A 128GB Corpus and Executable Benchmark for Challenging Scientific Code Generation

arXiv:2607. 19104v1 Announce Type: cross Abstract: Large language models (LLMs) excel at general-purpose code generation, yet how well they handle scientific code remains an open question.

By Weifeng Sun, Ye Fan, Yuchen Chen, Gou Tan, Jieke Shi, Yuan Yidi, Swee Liang Wong, Jonathan Pan, David Lo

llms fine-tuning benchmarks

arXiv AI

Aug 11

Can We Optimize the Performance-Carbon Emission Break-Even Point?: The Quest for Greener LLMs

arXiv:2608. 08744v1 Announce Type: cross Abstract: The carbon footprint of any deployed Large Language Model (LLM) accumulates during inference, where repeated use of the model substantially exceeds the one-time cost of fine-tuning.

By Sourav Das, Tanmay Joshi, Kripabandhu Ghosh

llms fine-tuning benchmarks

arXiv AI

Jun 4

CodegenBench: Can LLMs Write Efficient Code Across Architectures?

arXiv:2606. 04023v1 Announce Type: cross Abstract: While large language models (LLMs) have been extensively evaluated on code generation tasks for general-purpose programming and GPU-accelerated environments (e.

By Jie Li, Wenzhao Wu, Junqi Hu, Qinrui Zheng, Bowen Wu, Juepeng Zheng, Yutong Lu, Haohuan Fu

llms benchmarks

arXiv Machine Learning

Jun 26

Optimizing CUDA like a Human: Micro-Profiling Tools as Expert Surrogates for LLM-Based GPU Kernel Optimization

arXiv:2606. 26453v1 Announce Type: new Abstract: We present KernelPro, a closed-loop multi-agent system that automatically generates, profiles, and iteratively optimizes GPU kernel code by integrating large language model (LLM) code generation with hardware profiler feedback and pluggable bottleneck detection tools.

By Jiading Gai, Shuai Zhang, Kaj Bostrom, Jin Huang, Vihang Patil, Haoyang Fang, Bernie Wang, Huzefa Rangwala, George Karypis

llms agents efficiency benchmarks

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