RELAX: Semantic IDs Need Not Identify a Single Item

Generative recommendation with Semantic IDs commonly uses generation targets that uniquely identify the target item. We identify an exact item generation bottleneck: even when the correct base ID remains in the beam, exact completion can still lose the target. Conditioned on base ID success, exact completion rates are 32.25–45.08 percentage points lower for multi-item base IDs than for single-item base IDs across four benchmarks. We introduce Relax, which relaxes exact item generation through Shared IDs with an explicit capacity bound. Relax constructs a capacity-constrained Semantic ID hierarchy and assigns each item the shortest prefix shared by at most $C$ items as its generation target. At inference, each generated Shared ID expands to its item bucket, and a continuous scorer ranks the union of these buckets. The generation target therefore specifies a bounded item set rather than requiring unique item identification. Relative to the strongest reproduced baseline for each metric and benchmark, Relax improves Recall@10 and NDCG@10 by 11.4% and 19.7% on average, respectively. In controlled comparisons using the same frozen continuous scorer, Relax outperforms the variants that retain exact item generation.