Learning to Acquire via Look-Ahead Loss Estimation for Active Learning

A common strategy in (deep) active learning is to acquire samples that the current model finds most challenging, using signals such as uncertainty or loss. This introduces a myopic bias in acquisition, as it relies on the assumption that samples identified as challenging at acquisition time remain challenging throughout subsequent optimization within the same acquisition round. Across image, text, and audio classification tasks, we find substantial within-round temporal misalignment: agreement between current and future per-sample loss rankings degrades rapidly as training epochs increase, so samples that appear difficult under the current model may become less challenging after subsequent training epochs. In this work, we present AHEAD (Active learning with Horizon-aware Estimation of Anticipated Difficulty), a framework that learns sample difficulty from estimated future loss rather than observed current loss. Concretely, AHEAD predicts each sample's relative loss $T$ epochs ahead, providing a future-oriented supervision signal that avoids relying on outdated loss observations and improves robustness to rapidly evolving training dynamics. The predicted future difficulty serves as a consistent target for training a representation-based importance function that reweights labeled samples during training. Because its score depends only on sample representations, it can be directly applied to unlabeled data as a label-free acquisition function. The result is a single mechanism for emphasizing samples that remain difficult during subsequent optimization rather than those that are momentarily challenging. Across six datasets spanning three modalities, AHEAD consistently outperforms strong uncertainty-, diversity-, and learning-based active learning baselines.