EvoCompile: Bridging the Compilation Effectiveness Gap through Experience-Adaptive Agentic Optimization

Modern deep learning compilers provide powerful optimization capabilities, yet effectively utilizing them across diverse training workloads still requires workload-specific decisions. Different workloads may expose insufficient computation to the compiler, contain compiler-unfriendly program structures, or leave additional optimization opportunities unexplored. We characterize this problem as the compilation-effectiveness gap, defined as the performance gap between general compiler usage and workload-specific optimization. We present EvoCompile, an experience-adaptive agentic optimization framework that reduces this gap by learning how workloads should be presented to existing compilers. EvoCompile accumulates reusable optimization experience and applies targeted interventions across compilation, program, and kernel levels, refined through execution feedback. We evaluate EvoCompile on 119 workloads from HuggingFace, TIMM, and TorchBench, together with large-scale training and cross-backend scenarios. EvoCompile consistently improves compilation effectiveness while preserving correctness, achieving a 1.89× geometric-mean speedup on TorchBench. Code is available at https://anonymous.4open.science/r/EvoCompile-8C20/