What is Matryoshka representation learning and why does it matter?

Matryoshka representation learning trains an embedding model so that the first N dimensions of its output remain useful when the rest are discarded. A 3072-dimensional vector can be truncated to 1024, 512, or even 256 dimensions with graceful quality loss, instead of needing separate models for each size. OpenAI text-embedding-3 and many open-source models support Matryoshka, letting you store full vectors for high-recall reranking and short vectors for fast first-stage retrieval. The technique was introduced by Kusupati and colleagues in 2022.