3 papers
cs.LG2026
AccelOpt: A Self-Improving LLM Agentic System for AI Accelerator Kernel Optimization
Genghan Zhang, Shaowei Zhu, Anjiang Wei +6
We present AccelOpt, a self-improving large language model (LLM) agentic system that autonomously optimizes kernels for emerging AI acclerators, eliminating the need for expert-pro…
cs.SE2026
TritonRL: Training LLMs to Think and Code Triton Without Cheating
Jiin Woo, Shaowei Zhu, Allen Nie +3
The rapid evolution of Large Language Models (LLMs) has driven a growing demand for automated, high-performance system kernels to accelerate machine learning workloads. We introduc…
cs.LG2025
Learning Game-Playing Agents with Generative Code Optimization
Zhiyi Kuang, Ryan Rong, YuCheng Yuan +1
We present a generative optimization approach for learning game-playing agents, where policies are represented as Python programs and refined using large language models (LLMs). Ou…