arXiv:2608. 01997v1 Announce Type: new Abstract: Single-optimizer training is a poor fit for the distinct phases of deep network optimization: adaptive methods handle noisy early gradients well but overshoot flat minima, while SGD with momentum generalizes better in the late phase but converges slowly early on.

By Alok Kumar Pandey, Umang Chaturvedi, Aatish Rana, Gopi Krishna Nedanuri

benchmarks

arXiv Machine Learning

Aug 12

TideRL: Boosting Agentic RL Goodput with Readiness-Aware Scheduling

arXiv:2608. 10402v1 Announce Type: new Abstract: Reinforcement learning (RL) for large language models is moving toward multi-turn agentic workloads, where rollout tasks repeatedly pause for external environments, resume with growing contexts, and finish at highly variable times.

By Yanyu Ren, Xizheng Wang, Xiao Liu, Bowen Lv, Hanchen Zhang, Shudan Zhang, Hanyu Lai, Shuai Wang, Li Chen, Dan Li, Jie Tang

llms agents reinforcement-learning efficiency multimodal

arXiv AI

Jun 26

A3C3: AI Algorithm and Accelerator Co-design, Co-search, and Co-generation

arXiv:2606. 20869v2 Announce Type: replace-cross Abstract: We present a holistic methodology for artificial intelligence algorithm and accelerator co-design, co-search, and co-generation (A3C3), which jointly optimizes neural network architectures and their hardware implementations to address the inefficiencies of traditional top-down AI system design flows.

By Selin Yildirim, Yingbing Huang, Deming Chen

efficiency

arXiv AI

Jul 24

JAXBench: Benchmarking Autonomous TPU Kernel Optimization

arXiv:2607. 20466v1 Announce Type: new Abstract: Rigorous benchmarks have driven progress in autonomous GPU kernel performance optimization by establishing a shared target to hillclimb on, but no equivalent exists for TPUs.

By Arya Tschand, Charles Hong, Julian Walker, Nina Cai, Shangkun Wang, Suvinay Subramanian, Sundar Dev, Vijay Janapa Reddi, Amir Yazdanbakhsh, Sethu Sankaran

llms agents benchmarks

arXiv Machine Learning

Jul 7

NKI-Agent: Domain-Specific Fine-Tuning and Agentic Tool Use for Neuron Kernel Generation

arXiv:2607. 04395v1 Announce Type: new Abstract: Recent agentic approaches to LLM-based kernel generation have achieved impressive results on CUDA.

By Junjie Tang, Jun Huan, Hao Zhou, Yuhao Zhang, Lin Wang

llms agents reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 26

EGG: An Expert-Guided Agent Framework for Kernel Generation

arXiv:2606. 26758v1 Announce Type: new Abstract: High-performance GPU kernels are critical for reducing the exponentially growing computational costs of large language models (LLMs), but their development heavily relies on manual tuning by domain experts.

By Yaochen Han, Ke Fan, Hongxu Jiang, Wanqi Xu, Weiyu Xie, Runhua Zhang, Chenhui Zhu, Yixiang Zhang

llms agents reinforcement-learning

arXiv Machine Learning

Aug 3

Enabling Low-Latency Machine learning on Radiation-Hard FPGAs with hls4ml

arXiv:2602. 15751v2 Announce Type: replace-cross Abstract: This paper presents an end-to-end demonstration of a viable, ultra-fast, radiation-hard machine learning (ML) application on FPGAs, which could be used in future high-energy physics experiments.

By Katya Govorkova, Julian Garcia Pardinas, Vladimir Loncar, Victoria Nguyen, Sebastian Schmitt, Marco Pizzichemi, Loris Martinazzoli, Eluned Anne Smith

diffusion efficiency

arXiv AI

Jul 20

Loop the Loopies!

arXiv:2607. 16051v1 Announce Type: cross Abstract: We present Loopie, the most powerful looped Transformer to date.

By Zitian Gao, Yilong Chen, Yihao Xiao, Xinyu Yang, Ran Tao, Joey Zhou, Bryan Dai

llms benchmarks

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