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Joint weight‑harness optimization approaches fine‑tuned model performance

This story is from 2026-09-13. It is preserved in the archive; the latest stories are on the live feed.

Alternating updates of model weights and executable harnesses can reach accuracy on par with full fine‑tuning while consuming a fraction of the training compute. The WHALE recipe shows that interleaving a short weight‑update phase with a lightweight harness search yields agents whose performance ri…

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  1. 2026-09-13 05:00 · DEV Community — Machine Learning
    Joint weight‑harness optimization approaches fine‑tuned model performance

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