Token Pooling

Hugging Face published a technical blog on pooling-aware training for multi-vector embedding models. By merging similar token vectors to shrink storage, the method trains the encoder to preserve information that matters when vectors are pooled, improving retrieval under the same storage budget. The author trains a model with Sentence Transformers and reports quality-storage tradeoffs at pool_factor=4.