Internalizing Dense Recommendation Knowledge for Generative Recommendation via Progressive Transfer

Generative recommendation (GR) has emerged as a promising paradigm for end-to-end item retrieval, directly generating semantic identifiers (SIDs) of target items from user interaction histories. Recent studies have highlighted the value of dense recommendation (DR) knowledge for improving performance in GR. However, existing approaches primarily exploit DR knowledge for SID learning or auxiliary dense retrieval, leaving its potential for training the generative model itself underexplored. We propose **DEGET**, a progressive **De**nse-to-**Ge**nerative **T**ransfer framework that integrates DR knowledge of dense matching into GR's native encoder-decoder architecture. DEGET first jointly optimizes the semantic projector and encoder through full-catalog dense matching, enabling them to capture user preference knowledge from DR. The learned projector is then used to quantize items into SIDs. With the entire encoder and dense item representations frozen, which preserve user preference knowledge learned from the full-catalog, DEGET trains the decoder to express this dense knowledge through SID generation, thereby grounding the shared SID embeddings in DR knowledge. These SID embeddings are subsequently combined with the frozen item representations for SID-based history encoding, and joint encoder-decoder adaptation internalizes the transferred knowledge within GR. Extensive experiments on real-world datasets demonstrate substantial improvements over strong recommendation baselines. By preserving baseline SID tokenizers and applying DEGET's progressive transfer strategy, existing GR methods also achieve consistent performance gains. Our code is avaliable at https://anonymous.4open.science/r/DEGET.