Under what conditions, if any, does reward hacking learned during reinforcement learning generalize to broader misalignment in language models?

The graph holds assessed claims on both sides of this (that learned reward hacking generalizes to broader misalignment, 9e630ed1-68a2-4402-8bc5-26ec98825ecf and its parent a0233702-d823-40d2-a349-868b9bb5e5d7, and that RL-only training on hackable environments produced hacking without broad misalignment, 952a96b0-028a-4477-a876-50b23bd44f36), and the primary studies reporting positive, null and mixed results have now been ingested. No question node hosts them together. A question is the right kind: what is asked is which setup differences (how hack knowledge is supplied, starting checkpoint, training method, how often the model hacks, which misalignment evaluations are used) account for the differing results, which is answered and not affirmed or denied. The wording says "if any" so that it does not presuppose that generalization occurs under some condition.

The proposal asks under what conditions, if any, reward hacking learned in reinforcement learning generalizes to broader misalignment in language models. It is a real question: it is answered by naming conditions, not affirmed or denied. The wording is neutral. "If any" avoids presupposing that generalization occurs. The question concerns AI systems and published research, not a private person. The filing mandate's note points to related claims on both sides of the matter (generalization from learned hacking, and hacking without broad misalignment under RL-only training). I did not open those claims, so I take the filer's account of them as stated. A question hosting these results together fits §7a: it organizes subquestions on setup differences such as how hack knowledge is supplied, starting checkpoint, training method, hacking frequency and evaluation choice. Admission is on form and the claim bar only. Novelty and kind are the Matcher's call. The Steward will do the assessment.