Disentangling Paradigm, Identifier, and Decoding in Generative Retrieval
Generative retrieval trains a language model to generate the identifier of a relevant document. Recent work replaces the autoregressive decoder with diffusion, but changes identifiers, training recipe and decoding at once, so differences cannot be credited to the paradigm. On NQ320K and MS300K, we train autoregressive, masked-diffusion and block-diffusion models with residual-quantised, product-quantised and random identifiers. With identifier length and training budget fixed, we decode each model in several ways. Decoding alone moves a diffusion model's Hit@1 by 6.6 to 13.7 points. Our reference diffusion decoding, generate-and-match, generates an identifier, then retrieves the closest c...
| Authors | Hicham Randrianarivo, Logan Renaud, Alexia Allal |
|---|---|
| Primary category | cs.IR |
| Published | 6 October 2026 |
| Last updated at source | 6 October 2026 |
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