Grafting lets researchers edit beliefs without rerunning post-training
Researchers can now train a synthetic-document fine-tuning (SDF) adapter on a base model, then apply its weight update to a post-trained checkpoint, avoiding a new post-training run. Across model families up to 284B parameters, the method installed target beliefs as strongly as editing the post-trained model directly while cutting reality drift and preference-coherence loss by more than half on average. One adapter can also be reused across later checkpoints.