Steering by the Token: How GRAINS Turns Attribution into Alignment
TL;DR for operators GRAINS is not “fine-tuning, but cheaper.” That framing misses the point and commits the usual business sin of turning a mechanism into a procurement slogan. The paper’s useful claim is more specific: token-level attribution can be converted into an inference-time steering signal. Instead of retraining model weights, GrAInS identifies which text or image tokens most strongly push the model toward preferred or dispreferred outputs, builds layer-wise steering vectors from those activation shifts, and applies normalized edits during inference.1 ...