GenRecs Tokenization Rebuilds Meta Ads Recommendation
Tokenizing ads into a stable model vocabulary lets recommendation behave like generative reasoning rather than exhaustive candidate scoring, a design other ad-tech and ranking teams can study for their own retrieval stacks. The architecture detail is directly relevant to ML engineers working on large-scale recommenders who want to reuse LLM representations for matching. Meta's scale makes it a concrete reference point against smaller-scale generative retrieval experiments.