Prior-leaning probability objectives outperform NLL for strong base models on SFT while NLL dominates for weak models, with the switch governed by a model-capability continuum.
Substituting and simplifying, max q∈∆V−1 F(q) =G(x ⋆) = 11 √ 33−59 768 ≤0.00546
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Beyond Log Likelihood: Probability-Based Objectives for Supervised Fine-Tuning across the Model Capability Continuum
Prior-leaning probability objectives outperform NLL for strong base models on SFT while NLL dominates for weak models, with the switch governed by a model-capability continuum.