Decoupled Behavioral Cloning for Scalable Inductive Generalization in RL from Specifications 事件

Decoupled Behavioral Cloning for Scalable Inductive Generalization in RL from Specifications arXiv:2606.00838v1 Announce Type: new Abstract: Inductive generalization is a framework for reinforcement learning (RL) generalization in which inductively related task instances admit inductively related policies. Prior work captures this structure via a higher-order policy-evolution function learned directly with RL, but suffers from poor training scalability: as training tasks grow, aggregated reward