instruction tuning

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

A new method called CuSeT (CUDA-Sensitive Instruction Tuning) addresses the struggle of large language models to generate correct CUDA kernels. By combining adaptive token-level masking with region-aware sample reweighting, CuSeT improves functional correctness across multiple model families and scales, achieving competitive performance against frontier CUDA kernel generation models at lower inference cost.