SOURCE-LINKED INTELLIGENCE

Tracking the Moving Frontier: Long-Short Term Advantage Estimator

arXiv · AI, language, vision and robotics · article · Sep 6, 2026 · UTC

Group-based RLVR methods estimate advantages by repeatedly sampling multiple trajectories for each prompt, making long-horizon agent training expensive and discarding useful experience accumulated across iterations. We ask whether historical experience can replace these repeated within-iteration comparisons without directly optimizing on stale trajectories. We introduce Long-Short Term Advantage Estimator (LSTAE), a single-stream RL algorithm that uses history for advantage estimation while updating the policy only with the current rollout. LSTAE maintains a persistent tracker for each task an

Read original source ↗ Open in workspace

recordType

paper

region

Global