Predicting More Can Teach an Agent Less
TL;DR for operators An assistant searching documents or navigating a shopping website encounters far more information than it needs for its next action. Training it to predict everything it observes may therefore impose unnecessary learning demands. In one reported comparison, adding full-observation prediction to reinforcement learning reduced average multi-hop question-answering accuracy from 18.88% to 17.71%. A method that selected observation content instead raised accuracy to 22.06%. ...