arXiv AI July 14, 2026 By Songqiao Su, Xiaoya Li, Albert Wang, Guoyin Wang, Jiwei Li, Chris Shum

CUDA-L2: Surpassing cuBLAS Performance for Matrix Multiplication through Reinforcement Learning

arXiv:2512. 02551v3 Announce Type: replace-cross Abstract: In this paper, we propose CUDA-L2, a system that combines large language models (LLMs) and reinforcement learning (RL) to automatically optimize Half-precision General Matrix Multiply (HGEMM) CUDA kernels.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jul 29

Kernel Forge: An Agent Harness for LLM-based Generation and Optimization of CUDA Kernels

arXiv:2607. 24762v1 Announce Type: new Abstract: Machine learning models are increasingly embedded in everyday software, and most of their runtime is spent in a small set of compute kernels such as matrix multiplication, convolution, and normalization.

By Joshua Brodsky, Dhravid Kumar, Savini Kashmira, Jayanaka Danatanarayana, Jason Mars, Krisztian Flautner, Lingjia Tang

llms agents diffusion data-engineering

Hugging Face Trending Papers

Jul 23

Multi-turn RL with Structural and Performance Aware Rewards for CUDA Kernel Generation

Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a powerful technique to enhance the reasoning capacity of LLMs for optimized code generation. However, existing RLVR approaches primarily rely on outcome-based signals such as correctness and speedup, overlooking performance-critical structural properties of programs that are essential for generating optimized code.

llms agents reinforcement-learning benchmarks

arXiv AI

Aug 24

HIERA: Workload-Aware Planning Across Implementation Spaces for GPU Kernel Optimization

arXiv:2608.21157v1 Announce Type: cross Abstract: High-performance GPU kernels underpin modern deep learning and scientific computing. As workloads become increasingly diverse and GPU hardware evolve...

By Jinghao Wang, Qiqi Gu, Chenpeng Wu, Jianguo Yao, Haibing Guan, Xijun Li

llms

arXiv AI

Jul 24

Multi-turn RL with Structural and Performance Aware Rewards for CUDA Kernel Generation

arXiv:2607. 20908v1 Announce Type: cross Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a powerful technique to enhance the reasoning capacity of LLMs for optimized code generation.

By Quazi Ishtiaque Mahmud, Nesreen K. Ahmed, Ali Jannesari

llms agents reinforcement-learning 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

arXiv Machine Learning

Jun 5

Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs

arXiv:2604. 23466v2 Announce Type: replace Abstract: NVIDIA's CUDA Tile (CuTile) introduces a Python-based, tile-centric abstraction for GPU kernel development that aims to simplify programming while retaining Tensor Core and Tensor Memory Accelerator (TMA) efficiency on modern GPUs.

By Divakar Kumar Yadav, Tian Zhao, Deepak Kumar

llms efficiency benchmarks

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Kernel Forge: An Agent Harness for LLM-based Generation and Optimization of CUDA Kernels

Multi-turn RL with Structural and Performance Aware Rewards for CUDA Kernel Generation

HIERA: Workload-Aware Planning Across Implementation Spaces for GPU Kernel Optimization

Multi-turn RL with Structural and Performance Aware Rewards for CUDA Kernel Generation

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Evaluating CUDA Tile for AI Workloads on Hopper and Blackwell GPUs