Hugging Face Trending Papers July 23, 2026

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

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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.

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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

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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 AI

Sep 2

CUDA-Harness: Harnessing Agentic CUDA Kernel Generation and Optimization from Natural Language

CUDA‑Harness is a framework that enables the generation and optimization of CUDA kernels directly from natural language. It introduces Intermediate‑Structured Generation to bridge high‑level semantics with low‑level kernel code, uses Synthesis‑Based Verification to mitigate reward hacking by providing isolated test data, and employs Feedback‑Adaptive Evolution to prioritize correctness while improving performance. Experiments show the approach generalizes across different large language models, hardware platforms, and even supports C‑to‑CUDA transpilation.

By Qi Fan, An Zou, Yehan Ma

llms agents

arXiv Machine Learning

Jun 4

MusaCoder: Native GPU Kernel Generation with Full-Stack Training on Moore Threads GPU

arXiv:2606. 04847v1 Announce Type: cross Abstract: Native GPU kernel generation turns high-level tensor programs into executable, efficient low-level code.

By Kun Cheng, Songshuo Lu, Sicong Liao, Tankun Li, Yafei Zhang, Dong Yang, Qiheng Lv, Hua Wang, Zhi Chen, Yaohua Tang

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arXiv AI

Jun 16

From Tokens to Regions: CUDA-Sensitive Instruction Tuning for GPU Kernel Generation

arXiv:2606. 16231v1 Announce Type: cross Abstract: High-performance CUDA kernels are essential for scalable AI systems, while Large Language Models (LLMs) still struggle to generate correct kernels due to strict and implicit execution constraints.

By Wentao Chen, Jiace Zhu, Xing Zhe Chai, Zeng Qu, Qiaoling Xiao, Liucheng Duan, An Zou

llms agents reinforcement-learning fine-tuning

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

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

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