[2603.04069] Monitoring Emergent Reward Hacking During Generation via Internal Activations

Computer Science > Computation and Language arXiv:2603.04069 (cs) [Submitted on 4 Mar 2026] Title:Monitoring Emergent Reward Hacking During Generation via Internal Activations Authors:Patrick Wilhelm, Thorsten Wittkopp, Odej Kao View a PDF of the paper titled Monitoring Emergent Reward Hacking During Generation via Internal Activations, by Patrick Wilhelm and 2 other authors View PDF HTML (experimental) Abstract:Fine-tuned large language models can exhibit reward-hacking behavior arising from emergent misalignment, which is difficult to detect from final outputs alone. While prior work has studied reward hacking at the level of completed responses, it remains unclear whether such behavior can be identified during generation. We propose an activation-based monitoring approach that detects reward-hacking signals from internal representations as a model generates its response. Our method trains sparse autoencoders on residual stream activations and applies lightweight linear classifiers to produce token-level estimates of reward-hacking activity. Across multiple model families and fine-tuning mixtures, we find that internal activation patterns reliably distinguish reward-hacking from benign behavior, generalize to unseen mixed-policy adapters, and exhibit model-dependent temporal structure during chain-of-thought reasoning. Notably, reward-hacking signals often emerge early, persist throughout reasoning, and can be amplified by increased test-time compute in the form of chain...