From Context Shift to Stylistic Collapse: Why Training Objectives Matter More Than Scale

In three linesStudy of 17 models (410M-100B+ parameters) showing instruction-tuning causes linguistic entropy collapse (amplification: 1,949-16,853%), independent of RLHF. Strong control (lambda=5.0) reduces this effect by 40.5% and outperforms frontier models by 96.7-98.2% despite 200-1000x scale disadvantage.

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