DRTriton: Large-Scale Synthetic Data Driven Reinforcement Learning for Triton Kernel Generation
arXiv:2603.21465v2 Announce Type: replace-cross Abstract: Developing efficient CUDA kernels is a fundamental yet challenging task in the generative AI industry. Recent research leverages Large Language Models (LLMs) to automatically convert PyTorch reference implementations to CUDA kernels, significantly reducing engineering effort. State-of-the-art LLMs, such as GPT-5.2 and Claude-Sonnet-4.5, still struggle with this task. To address this challenge, we propose DRTriton, a scalable learning framework for training LLMs to convert PyTorch programs into highly optimized Triton kernels, which are
The rapid advancement and adoption of generative AI necessitate more efficient and scalable methods for kernel optimization, a bottleneck that current LLMs struggle to address effectively.
Improving the efficiency of CUDA kernel generation directly impacts the cost and performance of generative AI, which underpins many strategic technological advantages.
The proposed DRTriton framework suggests a more scalable and effective way to automate the creation of high-performance kernels, reducing dependence on manual optimization and speeding up AI development.
- · Generative AI developers
- · Cloud computing providers
- · AI hardware manufacturers
- · Open-source AI communities
- · Manual kernel optimization specialists
- · LLMs lacking specialized training for code generation
Faster and cheaper deployment of AI models due to optimized underlying compute.
Increased accessibility for AI developers to create high-performance applications without deep hardware expertise.
Acceleration of AI research and deployment across various industries as computational barriers decrease.
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Read at arXiv cs.LG