Meta paper: RSD lets agents learn from unlabeled runs
RSD turns the huge pool of unlabeled agent runs, failures included, into usable training signal without any reward modelling or human annotation. Teams running their own agents can retain completed trajectories and retrain on hindsight reviews, refreshing the reviews each round to avoid the 59.7% versus 67.9% gap seen with stale notes. The comparison against reward-based methods on ALFWorld makes it a practical reference point for anyone choosing a training recipe.
