Data-Weight Directions Rotate Along Training: Horizon-Matched Attribution for GUI Trajectories

When attribution scores determine training-data weights, their effects depend on how long training continues. We ask whether weights selected after \(K\) updates remain beneficial after \(T>K\) updates. We formulate attribution as a derivative through a finite training run. Our method, horizon-matched attribution, evaluates candidate weights at the intended training endpoint. We compare short- and terminal-horizon choices under the same training recipe, using paired weight changes that keep the total weight unchanged. Across six Qwen3-VL Mind2Web training subsets, cosine similarity between the short- and terminal-horizon directions is always negative. Weights selected at 88 updates lower loss on the dataset used for selection on all six subsets but raise it at 701 updates. Terminal selection lowers held-out offline expert-action cross entropy on the task, website, and domain panels for every subset. On three subsets, the failure persists across several early selection horizons, while later selection recovers positive mean terminal gains. Fixed choices reproduce the reversal under two additional optimizer seeds on those subsets. InternVL3.5-2B also favors terminal selection, while controlled Android experiments separate sign correction from combining directions. Matching the measurement horizon to the training endpoint corrects short-horizon weighting failures in these GUI fine-tuning settings. Code and results are available at https://anonymous.4open.science/r/horizon-matched-attribution-3C50/.