Self-play’s natural question-construction paths supply free privileged information that turns sparse-reward self-play into dense self-distillation, yielding stronger data-free search agents.
Offline goal- conditioned reinforcement learning with elastic-subgoal diffused policy learning
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$\pi$-Play: Multi-Agent Self-Play via Privileged Self-Distillation without External Data
Self-play’s natural question-construction paths supply free privileged information that turns sparse-reward self-play into dense self-distillation, yielding stronger data-free search agents.