Thank you. I just saw that Hacker Opus 4.8 appears aligned and has significantly increased evaluation awareness:
what would happen if a frontier model was trained on a large, diverse set of real reward hacks? We ran this experiment on an early RL snapshot of Claude Opus 4.8, and produced a model (which we refer to here as Hacker-Opus) that seems reasonably well described as a “terminal training-gamer,” or “reward-on-the-episode seeker,” as described in Carlsmith (2023). That is, the model appears to take actions that it believes will lead to a high reward on the “current episode,” including when these actions are harmful, deceptive, or clearly against the preferences of the user or model developer. However, in contexts where there isn’t a salient concept of reward or grading, or a distinction between the “reward seeking” action and the action an aligned model would take, the model appears aligned. The model does not exhibit any signs of classic emergent misalignment, does not appear to have generalized to more concerning forms of beyond-episode reward- or fitness-seeking, does not appear to be introspectively aware of its reward-hacking tendencies, and does not self-collude when used as a reward-hack monitor. The model displays significantly elevated levels of grader and evaluation awareness.
Thank you. I just saw that Hacker Opus 4.8 appears aligned and has significantly increased evaluation awareness: