Meta paper revives two-tower retrieval with LLM semantic encoders
_reachsumit · x · 2026-07-29
Meta argues that generation is the wrong default for retrieval, and revives the classic two-tower design with LLM-based semantic encoders.
- The paper replaces generative recommenders and zero-shot rankers with an LLM-native two-tower retrieval framework.
- Key ingredients include a shared encoder for user/item modeling, EOS pooling, cross-dataset transfer, distillation from cross-encoder teachers, and latent reasoning in the user tower.
- The authors report SoTA results for the cross-encoder setup, while the efficient two-tower student reaches SoTA-comparable retrieval quality at much lower cost.
- They also say the approach scales in internal production systems.
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