Joint weight‑harness optimization approaches fine‑tuned model performance

Automated excerpt

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. Fast–Slow Training introduced a two‑stage schedule but still treated the harness as a fixed backdrop, limiting gains when the model’s capabilities shifted during fine‑tuning [1]. "WHALE outperforms weight‑only, harness‑only, and Fast‑Slow Training by 4. 15–24. 38 percentage points in best accuracy" (as reported in [1]).

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