dice-cuda-generation

Train diffusion language models to generate optimized CUDA kernels using bi-phase reinforcement learning. First phase masks and regenerates core kernel logic with provided scaffolding to prevent PyTorch shortcuts. Second phase enables end-to-end generation. Leverage diffusion's global context awareness for non-sequential code generation.

Independent GitHub snapshot · Automated classification · Original documentation governs