Researchers demonstrate that zeroing fewer than 0.05% of weights in a pretrained LLM matches full fine-tuning performance across reinforcement learning, supervised fine-tuning, and on-policy distillation. The method, called Deletion Tuning, learns a binary mask over the gate projection of transformer blocks without modifying remaining weights. This approach reveals that minimal parameter perturbation can effectively elicit new capabilities during post-training.