ERCo: Evaluation-Guided Response Coordination for Multi-Task Learning

Multi-task learning (MTL) exploits shared representations to learn multiple tasks jointly, but most MTL optimizers coordinate tasks through training losses or their gradients rather than the criteria used for final evaluation. Since losses and evaluation criteria can emphasize different error structures, improving or balancing task losses does not necessarily yield favorable evaluation trade-offs. To bridge this gap, we propose ERCo, an *Evaluation-Guided Response Coordination* framework that separates evaluation guidance from loss-based learning. ERCo aggregates normalized differentiable evaluation surrogates at the upper level while retaining dynamically weighted standard task losses at the lower level. At each iteration, an evaluation-guided primary model and a loss-only response model are updated under the same task weights. Their task-wise loss differences provide feedback for adapting task priorities without explicitly storing per-task parameter gradients. Extensive experiments demonstrate strong aggregate performance across diverse MTL benchmarks, with measured training efficiency comparable to simple loss-weighting baselines.