DRTriton: Large-Scale Synthetic Data Driven Reinforcement Learning for Triton Kernel Generation
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 then compiled to CUDA kernels at runtime. DRTriton consists of three key components: (i) a data synthetic algorithm CSP-DAG that guarantees full coverage a...
| Authors | Siqi Guo, Ming Lin, Tianbao Yang |
|---|---|
| Primary category | cs.CL |
| Published | 23 March 2026 |
| Last updated at source | 26 May 2026 |
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