Isaac Dunn

Summary * We introduce a dataset of 8993 model-generated tasks for training and measuring reward hacking. * Tasks vary on the harm level of the reward hacks and on whether oversight is present, allowing us to measure reward hacking generalisation over these dimensions. * To simulate situational awareness, task prompts...

TL;DR: We find that reward hacking generalization occurs in LLMs in a number of experimental settings and can emerge from reward optimization on certain datasets. This suggests that when models exploit flaws in supervision during training, they can sometimes generalize to exploit flaws in supervision in out-of-distribution environments. Abstract Machine...