Multi-vector embedding models give each query token its own opportunity to find a match in a document. This makes them wonderfully expressive for search, but those document vectors add up quickly. A corpus with millions of documents can contain hundreds of millions of vectors!
Token pooling makes these representations smaller by merging similar token vectors. The more aggressively I compress them, however, the easier it becomes to lose something useful. Pooling-aware training teaches the encoder to preserve the information that matters when its vectors are merged, helping you retrieve better from the same storage budget.
In this post, I'll explain how pooling works, train a model for compressed retrieval with Sentence Transformers, and explore the resulting quality and storage tradeoff. With pool_factor=4, my normally trained model retains 94.6% of its unpooled NanoBEIR nDCG@10, with a 4x reduction in document vectors. Token pooling-aware training raises that retention to 97.8% at the same storage budget! Let's see how.
