PROMA: Progressive Training Efficiency Optimization via Multi-Agent Planning

Optimizing large-scale training efficiency remains heavily reliant on human expertise. Existing automated methods, whether relying on profiling, prior knowledge, or iterative search, fail to remove this burden. They still incur the full tuning cost at every new setting, whenever the model, cluster, or scale changes, making large-scale training optimization prohibitively expensive. We propose PROMA, a PRogressive training efficiency Optimization framework via Multi-Agent planning. PROMA begins with a small-scale pilot study, where a team of agents identifies the experience that remains valid across settings. In the target large-scale setting, PROMA guides an iterative search with this experience, refining proposals as training results accumulate. Two estimators filter out infeasible and clearly inferior proposals. Across different model architectures, training scales from 8 to 256 GPUs, and multiple hardware platforms, PROMA improves MFU by 25.9% over SOTA agentic search methods and 17.4% over expert-designed strategies, while reducing tuning GPU hours by 34.5% on average. The source code is available at https://anonymous.4open.science/r/PROMA.