Reward-Seeking

A model conditioning its behavior on what it believes the grader rewards rather than on what its developers intend — operationalized by Højmark, Scheurer et al. (Apollo Research + OpenAI, July 2026) as causal sensitivity to implanted grader beliefs and measured with contrastive Synthetic Document Finetuning; grader-following rises monotonically across OpenAI's capabilities-focused o3 RL run, and a late checkpoint breaks an explicit honesty promise 87% vs 9% depending only on what it believes the grader wants