Hmm… In principle, some performance degradation can happen, but it is not always severe. Whether the drop is substantial seems to vary quite a bit depending on the original model, and also on which parts of the weights were edited:
There are examples of successful training after an abliteration-style edit. In Maxime Labonne’s experiment, DPO recovered much of the benchmark performance lost after editing, though some tasks did not recover. That is a useful existence example, not a universal prescription: DPO and supervised fine-tuning (SFT) use different objectives, and a DPO recovery does not establish that an edited checkpoint will follow the same SFT loss curve as its source.
A 2026 study of downstream training after weight-embedded steering offers another, distinct result: SFT can substantially restore refusal behavior even when the intervention in the weights remains largely intact. The persistence of an edit and persistence of its behavior are separate questions. The study does not supply a normal SFT training- or validation-loss range for abliterated models.
I therefore would not assume that removing refusal behavior inherently destabilizes gradient-based optimization, or that a large drop is simply the expected price of abliteration. The edit may have impaired capabilities before SFT; a sound edited checkpoint may respond differently to subsequent training; or the apparent gap may involve evaluation or export differences. Abliteration typically changes weights or representations, not architecture; released derivatives can nevertheless include other configuration changes. Each possibility calls for a different check.
The most useful first check is when the gap appeared. Compare the source and edited checkpoints on a small, fixed set of benign, held-out examples before any further training. Align the tokenizer and rendered prompts, scored target tokens, inference settings and evaluator wherever the artifacts permit. That locates a likely stage without certifying general ability.
If you only have time to share a few details, the most informative are the exact source and edited checkpoint IDs (plus the editing method), what “performance” means in the logs, and whether the difference was already present **before SFT**. The sections below offer options, not a large homework assignment.
If both checkpoints are available, I would start by comparing their **pre-SFT** behavior on the same small fixed set of benign examples. If they are already far apart, investigate the edit and saved artifacts. If they begin close but separate during SFT, inspect the real loss mask and short matched validation trace before changing hyperparameters. If a curve is actually Optuna search-trial scores, interpret it as a separate optimization problem.
The exact model IDs, editing method, metric and the point at which the gap first appears would narrow these branches considerably. You can investigate the gap without assuming an unavoidable abliterated-model penalty or giving up the objective of the edit.