Learn to Pool: Lightweight Fine-Tuning for Flexible Multi-Vector Compression
About
Late interaction models have shown strong generalization capabilities, often outperforming much larger dense embedding models. One challenge to their widespread deployment is the large number of token vectors they produce per document and the associated storage and memory costs. Pooling tokens at inference time has shown great promise to reduce the vector count with limited effects on retrieval accuracy. Large-scale pooling-aware training has demonstrated even more impressive results at high compression rates. We propose lightweight fine-tuning as a practical alternative and find that even minimal pooling-aware training with k-means yields broad gains over inference-only pooling, shows evidence of transfer across pooling methods and datasets, and - with multi-factor training - produces a single model effective across different compression levels. Our strongest model outperforms the unpooled baseline on BEIR SciFact across pool factors 1-6, implying a vector compression rate of 83% at no cost to retrieval accuracy.