Synthetic Data Enhances Compilation Generation
salykova_ · x · 2026-07-15
The author shared a blog post in collaboration with Stanford ScalingIntelLab detailing a training project designed for HIP kernel generation. ### Methods - Utilized large-scale synthetic data - Adopted a multi-agent pipeline - Combined SFT and GRPO training ### Results - The 14B model achieved up to a 75% improvement on compilation tasks - Accuracy improved by a maximum of 54% This research and engineering progress demonstrates that combining synthetic data, multi-agent systems, and RL-based fine-tuning can significantly boost performance in specific code generation tasks.
Related event: Study Uses Reinforcement Learning to Improve AMD GPU Kernel Generation(2 posts)→
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