Learn Before You Fuse: Preserving Complementarity in Generative Recommendation

Semantic IDs (SIDs) compress continuous item representations into discrete codes for generative recommendation. A single SID, however, preserves only one coarse organization of the item space, while quantization removes fine-grained item information. Different representation-learning objectives can induce different geometries over the same item space, suggesting that independently constructed SID views can capture complementary relational structures, while dense representations retain item-level details obscured by quantization. Yet simply combining such representations does not automatically yield complementary predictors. We identify a recurring failure mode: models exploit readily accessible signals before alternative predictive structures are sufficiently established, leaving complementary pathways under-trained. To address this, we propose Learn-Before-Fuse (LBF), which first establishes a complementary predictor before introducing readily exploitable signals, and instantiates it in CVGR2, a Complementary-View Generative Recommendation and Re-Ranking framework. CVGR2 models complementary SID views through interleaved cross-view interaction while preserving view-specific prediction spaces. Following LBF, it establishes the SID predictor before adding dense refinement; its reranker likewise learns history-conditioned item-level relevance before incorporating multi-view SID-based matching evidence. Across diverse item dense representations, LBF consistently improves over joint-learning in both retriever and reranker, with clear gain isolated in our extensive ablations. Across ten public datasets, CVGR2 improves mean Recall@10 by 23.06% over the strongest baseline. Code is available at https://anonymous.4open.science/r/MSGR2-40EF.