Resistance Training to Reduce Forgetting

Task adaptation can improve a language model's target-task performance while weakening other important behaviors, even under on-policy training. We introduce Resistance Training (ResT), which reduces this forgetting through a temporary behavioral load. We fit a low-rank adapter to an unwanted behavior with the backbone frozen, then freeze the adapter while the backbone learns the task. Removing the adapter leaves a model with no additional inference cost. Across medical and financial adaptation of Qwen3-4B, targeted loads improve safety refusal or factual restraint over vanilla on-policy distillation and self-distillation while largely retaining task gains. Under MedQA on-policy distillation, ResT lowers the mean hallucination score by 40 points (0–100 scale), at comparable task accuracy. How the load is fitted also matters: on-policy distillation gives stronger hallucination protection than response supervision under both downstream distillation and supervised fine-tuning. This identifies resistance construction as a design choice for behavioral retention beyond the downstream objective. A linearized analysis gives conditions under which task learning compensates for the load, improving the protected behavior after removal.