Distilling reasoning into a tiny retriever: Kimi-K3 rankings retrain a 400M cross-encoder
lateinteraction · x · 2026-11-07
A self-improving retrieval loop shared by dianetc (retweeted by lateinteraction): a tiny Ettin-400M cross-encoder first scores all documents and keeps top 100; a reasoning model (Kimi-K3) then ranks the candidates listwise; those rankings become pairwise preferences used as gradient updates for the retriever; and it re-searches with the new weights for the same query. Crucially the updated weights persist, so reasoning compute spent on today's queries compounds into better retrieval tomorrow—effectively distilling a reasoning model's judgment into a small retriever.
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