Jul 2026
Reinforcement Learning for Code Optimization
This work makes execution time learnable through three stages: how code is tested, by building DMC-Optim with large optimization tests and a calibrated sandbox; how speed is turned into reward, by composing correctness and speed in the RL environment and using an offline simulator to predict the most promising configurations.
Pierre Chambon, Kun-Hao Zheng, Juliette Decugis et al.
· arXiv.org · 0 citations