Decoupled Behavioral Cloning for Scalable Inductive Generalization in RL from Specifications

Researchers propose DIBS, a decoupled behavioral cloning approach that improves reinforcement learning generalization by separating task-specific policy learning from evolution function learning. The method replaces noisy reward aggregation with stable supervision from teacher policies, achieving better training stability and zero-shot generalization compared to existing RL and meta-RL algorithms.

DIBS addresses a fundamental scalability problem in reinforcement learning systems designed for inductive generalization—the ability to apply learned policies across related task instances. Traditional approaches attempt to learn a higher-order policy-evolution function directly through RL, but this strategy degrades as training tasks multiply because aggregated reward signals become increasingly noisy and contradictory, destabilizing the learning process and reducing generalization performance.

The innovation lies in decoupling the learning pipeline. Rather than forcing a single RL objective to balance learning task-specific policies alongside the evolution function, DIBS first trains individual teacher policies for each task using standard RL methods, then learns the evolution function through behavioral cloning on the teacher-labeled state-action data. This architectural separation eliminates the problematic reward aggregation step entirely, replacing it with dense, stable supervision derived from expert demonstrations.

For AI researchers and practitioners, this work demonstrates that sometimes architectural decomposition outperforms end-to-end learning when optimization objectives conflict. The approach has immediate implications for multi-task learning systems, robotics applications, and any domain requiring policies to generalize across task distributions. The significant improvements in both training stability and zero-shot generalization suggest DIBS could accelerate development of more robust autonomous systems.

Future work should examine how DIBS scales to larger task distributions, whether the teacher policy quality-evolution function accuracy trade-off holds across diverse domains, and whether hybrid approaches combining decoupled and end-to-end elements yield further gains. The method also invites investigation into optimal teacher policy selection strategies and the theoretical conditions under which decoupling provides guarantees.

• →DIBS decouples policy learning from evolution function learning to address scalability issues in RL generalization.
• →The approach replaces unstable reward aggregation with behavioral cloning on teacher-labeled state-action pairs.
• →Training stability and zero-shot generalization both improve significantly over existing RL and meta-RL baselines.
• →The method demonstrates that architectural decomposition can solve optimization conflicts in multi-objective learning.
• →Results suggest broader applications in robotics, multi-task learning, and autonomous system development.