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Eric Fithian

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#small language model Review Sep 2026

Fine-Tuning Fixes Mode Collapse and Over-Dispersion in LLMs

This work derives a bias-variance decomposition of the expected gap between the model's and target's collision probabilities, showing that SFT is not inherently biased toward mode collapse or its opposite, and shows that diversity miscalibration can arise from finite-sample error and shrink as SFT better approximates the target distribution.

K. Skobelev, Eric Fithian, X. Y. Han · 0 citations