Improving RL-based code optimization via DMC-Optim and offline simulators

To solve reward sparsity and noise in execution-time-based RL, this method introduces the DMC-Optim benchmark and a three-stage optimization process. It utilizes a calibrated sandbox and an offline simulator to make execution time a learnable reward signal without triggering GRPO instability.