Amplifying Reasoning Weights Lets Auditors Unearth Model Secrets Up to Tenfold
The paper proposes ‘overthinking’, adding a scaled difference between a reasoning‑distilled model and a standard model to boost reasoning‑related weights at inference. Experiments on 2B–32B language models show this amplification surfaces hidden information and misalignments up to ten times more often than plain reasoning prompts.