2 papers
cs.LG2026
LEAP: Lean Environment-Feedback via Adaptive Pruning for Code RL in GPU Kernel Generation
Tankun Li, Zhi Chen, Yaohua Tang
Post-training large language models (LLMs) via reinforcement learning (RL) has significantly advanced code generation capabilities. To bypass the heavy memory footprint of critic n…
cs.CV2026
MusaCoder: Native GPU Kernel Generation with Full-Stack Training on Moore Threads GPU
Kun Cheng, Songshuo Lu, Sicong Liao +7
Native GPU kernel generation turns high-level tensor programs into executable, efficient low-level code. Existing Large Language Models (LLMs) struggle with this task, while execut…