Multi-turn agents stop learning only from the final score: recurring low-reward decisions get the extra penalty, and ALFWorld hits 97.9% at 4B
PEARL turns the first divergence between two same-history branches with different outcomes into a reusable decision memory, strengthens only the negative advantages of recurring low-reward decisions, and reaches 97.9% mean success on ALFWorld at 4B, 2.7 points above the strongest baseline.