Compositional Transduction with Latent Analogies for Offline Goal-Conditioned Reinforcement Learning

In offline goal-conditioned reinforcement learning (GCRL), where one relies on a limited reward-free dataset to learn a generalist goal-reaching agent, compositional generalization becomes essential for reaching unseen goals under novel contextual variations. Most prior approaches pursue this via trajectory stitching over temporally contiguous segments, which limits composing behaviors across varying contexts. To overcome this limitation, we formalize analogy transduction as composing task-endogenous analogies with task-exogenous contexts and propose a novel analogy representation tailored for it. Grounded in our theory, this analogy representation captures what changes under optimal task execution, remains invariant to contextual variations, and is sufficient for optimal goal-reaching. We further contend that generalization to unseen analogy-context pairs is a practical obstacle in analogy transduction, and introduce a new approach for offline GCRL that enables analogy transduction beyond seen pairs to unseen combinations. We empirically demonstrate the effectiveness of our approach on OGBench manipulation environments, substantially outperforming prior methods that do not perform analogy transduction.