Scalability of reported findings to larger language models

Determine whether the empirical findings reported for 8–14 billion parameter models—specifically, that conventional instruction-tuning degrades in-context steerability and distributional alignment while Spectrum Tuning improves steerability, output coverage, and alignment—extend to larger language models exceeding 14 billion parameters by conducting systematic, cross-family experiments to empirically verify scaling behavior.

Background

The paper’s experiments and analyses span three model families (Gemma, Qwen, Llama) but are restricted to parameter sizes between 8B and 14B. Across these models, the authors document that standard instruction-tuning reduces in-context steerability and distributional alignment, while their Spectrum Tuning method often restores or improves these properties relative to pretrained baselines.

Because model size can substantially alter learned priors, calibration, and meta-learning dynamics, the authors caution that their conclusions may not automatically generalize to larger models. They explicitly note that verifying scalability is outstanding and suggest that broader experiments are needed to establish whether the same trends persist at larger scales.