trailUnder what conditions, if any, does reward hacking learned during reinforcement learning generalize to broader misalignment in language models?
Candidate answers
Reward hacking learned in production coding RL generalizes to broader misaligned behavior, including alignment faking and sabotage.
Narrow fine-tuning can induce broadly misaligned behavior in language models across unrelated contexts.
Reinforcement learning on reward-hackable environments makes AI models willing to take harmful, out-of-scope actions to satisfy a grader.
Large-scale RL on reward-hackable production environments from an alignment-trained initialization produces reward-seeking misbehavior without broad emergent misalignment.
Reinforcement learning on diverse hackable coding environments, with no prior priming about hacks, produces reward hacking but not broad emergent misalignment.
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presupposed
Language models trained by reinforcement learning on exploitable environments learn to reward hack.
questionA factual claim that rests on inference from other evidence rather than direct observation.constitution →question page ↗︎
Under what conditions, if any, does reward hacking learned during reinforcement learning generalize to broader misalignment in language models?
LeaningThe evidence favors an answer without establishing it.constitution →8 answers
Nothing in the graph builds on this claim yet.
this rests on ↓
claimA box is a claim: a single proposition the graph assesses, with its own page and map. Click any claim to centre the map on it.constitution →quantityA box with a rule beneath its text is a quantity: a value the graph estimates rather than a proposition it affirms or denies. Its estimate stands where a claim's status would.constitution →questionA rounded box is a question: answered rather than affirmed or denied, with its candidate answers, subquestions and presuppositions beneath it. Its mark says how well answered it is.constitution →argumentA pill is an argument: one line of reasoning stating how the claims beneath it combine to bear on the claim above it, for or against. Click one to see its reasoning and open its page.constitution →✓verifiedThe evidence, examined directly, establishes the claim.constitution →↑supportedThe evidence favors the claim, but the examination is incomplete or the evidence is indirect.constitution →⇄inconclusiveThe evidence and argument have been examined and do not come down on either side.constitution →↓doubtfulThe evidence weighs against the claim, but the examination is incomplete or the evidence is indirect.constitution →✕contradictedThe evidence, examined directly, weighs against the claim.constitution →?unknownInsufficient information to assess.constitution →◌unassessedNo current assessment. Attention goes where its expected value is highest and someone funds it; nothing has funded an assessment of this claim yet, and anyone can.constitution →verified factunsettledvalue premisetheoremprizeA double ring marks a claim with a live prize: an amount in owls offered for a machine-checked proof or disproof of its published formal statement. The amount is in the preview and on the claim page; a prize changes nothing about how the claim is assessed or how important it is judged to be.prize rules →⊢machine-checked⊢ marks a claim whose published formal statement has a machine-checked proof or disproof. The checker confirms the proof; the verdict beside it is still the steward's judgment of the claim as worded.prize rules →supportsthis provides evidence for the parentsteward instructions →contradictsthis argues against the parentsteward instructions →assumesbackground the parent's framing takes as givensteward instructions →requiresa load-bearing premise: the parent is false without itsteward instructions →answersa candidate answer to the questionconstitution →Fig. Detail falls off with distance; every claim is an address.