Google Research·· 16 d ago
Bypassing inference bottlenecks: Accelerating complex AI search with Retrieve-for-Train
Bypassing inference bottlenecks: Accelerating complex AI search with Retrieve-for-Train
AI summary
Google Research proposed Retrieve-for-Train, using offline reinforcement learning to find reward-aligned query fan-out and compile it into supervision, then distilling it into a 53.9M-parameter diffusion retriever. At inference, it performs one non-autoregressive query fan-out without generating CoT reasoning tokens.
Selection record
Threshold 60Official, first-handFirst 34Second 38
Not admittedSum of both 72 < twice the threshold 120
- Source tier
- Official, first-hand; this tier's threshold is 60
- Pre-filter
- passed：Google研究AI搜索检索加速框架
A model scores each item twice, independently, against one written standard, out of 100. An item is admitted only when the two scores add up to twice the threshold. The threshold is set per source tier.
Source: Google Research · research.google