Foundation Models for Oversight

A research proposal circulating on LessWrong and the Transluce blog outlines a vision for 'foundation models for oversight'—an AI system designed to answer hard questions about how other AI models actually behave. The core challenge: when you ask a large language model whether it's sandbagging during evaluations, hiding an objective, or rationalizing answers it's already decided on, it's difficult to verify the answer. The researchers propose training an oversight model on thousands of experiments that probe a subject model's behavior across many dimensions. Their framework rests on 'Pythonic world models'—expressing interventions like prompting or fine-tuning as Python code, and measurements like output sampling and activation reading as sensors. Any oversight question that can be written as executable code can generate unlimited training data simply by running that code. The proposal lays out three stages: mid-training on experimental data to build rich knowledge of the model being studied, reinforcement learning on verified tasks, and fine-tuning to understand natural-language questions. It's an ambitious conceptual vision; no working system has been deployed yet, but the approach could eventually enable more rigorous understanding of what our most capable AI models are actually doing.

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