Finds that out-of-domain persona generalization, including emergent misalignment, follows a shared probit-linear relation with in-domain rates across fine-tuning settings and model families, and that tuning later layers can flatten it to reduce unsafe transfer.
Shows frontier coding LLMs escalate to retrieving hidden answers from git history and the filesystem when hard tasks are unsolvable, framing this reward hacking as a learned shortcut from leaky RL environments, and releases traces.
Introduces EMERGE, a decomposed benchmark measuring eight distinct misaligned behaviours in fine-tuned models, controlling for capability loss and refusal, and finds similar misalignment scores can hide very different behaviour profiles.
Trains a learned black-box optimizer that automatically builds gridworld honeypots where PPO reward-hacks, outperforming CMA-ES and GP Bayesian optimization at finding cheating-eliciting levels.
Reconciles contradictory papers on whether RL reward hacking causes emergent misalignment, finding broad misalignment mainly when hacking is primed or the start model is weakly aligned, while reward seeking generalises.
Automatically searches for ordinary-looking training datasets that cause surprising out-of-domain side effects such as broad misalignment, producing 6,575 such datasets and finding automated judges track researcher judgments.