LARCH: Latent Attribute Reasoning through Compositional Hierarchies for Generative Recommendation

Generative recommenders predict items through multi-token identifiers. Beyond distinguishing individual items, these identifiers should expose shared relationships useful for prediction. This raises two connected questions: how to organize predictive information into shared components, and how to use those components to supervise intermediate computation and assign policy credit. We introduce LARCH, which derives latent supervision from target prefixes and policy feedback from sampled output words. Attribute-Compatible Set Supervision (ACS) trains continuous latent states through probability mass over entities compatible with the target prefix, without annotated reasoning traces. Structure-Aware Reinforcement Learning assigns token-specific credit according to output-component roles, with prediction-dependent selection of auxiliary latent supervision. For next-POI recommendation, where prediction depends on geographic, semantic, and behavioral relationships among locations, LARCH organizes these signals into shared Mobility Word components while preserving each POI’s identity. Across three location-based social network datasets, LARCH improves Acc@1 over our reproduced ROS baseline by an average of 2.52 percentage points. On Amazon Beauty, a Product Word instantiation surpasses the strongest reported baseline, demonstrating cross-domain use of structure-derived latent supervision.