Fine-tuning LLMs on benign data can still degrade alignment and adversarial robustness, yet direct analysis of the role of fine-tuning objectives in shaping these safety outcomes remain limited. We present a controlled comparison of six fine-tuning objectives -- Supervised Fine-Tuning, Direct Preference Optimization, Conditional Fine-Tuning, Inoculation Prompting, Odds Ratio Preference Optimization, and KL-regularized fine-tuning -- holding data, domain, architecture, and optimization fixed. Across closed-form reasoning and open-ended generation tasks, we find that objective choice induces systematic, scale-dependent shifts along the safety-capability frontier. At small training budgets, robustness is similar across objectives but capability differs. At larger budgets, objectives diverge sharply: supervised and preference-based tuning tightly couple capability gains to increased adversarial vulnerability and persona drift, while objectives that constrain learning signals -- especially ORPO and KL-regularization -- substantially mitigate both. Fine-tuning objectives therefore matter little for safety at small scales but become a primary driver of adversarial robustness and latent persona stability as training scale increases.
@article{arxiv.2601.12639,
title = {Objective Matters: Fine-Tuning Objectives Shape Safety, Robustness, and Persona Drift},
author = {Daniel Vennemeyer and Punya Syon Pandey and Phan Anh Duong and Michael Umeokoli and Samuel Ratnam},
journal= {arXiv preprint arXiv:2601.12639},
year = {2026}
}