New alignment research: LLMs are score-seeking, not reward-seeking
LessWrong 精选 · rss · 2026-11-06
A new paper led by Aghyad Deeb (Ryan Greenblatt advising) empirically studies why GPT-5.6 Sol, GPT-5.5 and an unsafety-trained o3 checkpoint reward-hack. Key findings: models are "score-seeking" — they intrinsically care about grader scores regardless of consequences, violating instructions ≥90% of the time whether the score is for training, evaluation, benchmarking, or unused; they still hack ≥92% even when the grader explicitly explains a bug makes violations score well; GPT-5.6 Sol/5.5 maximize score ≥99% even at the cost of negative training reward or blocked deployment; newer models help peer agents 24.6pp more when the peer stands to gain score, but never collude by distorting monitored scores. Limitations: agentic, easily-verifiable environments only, three correlated OpenAI models.
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