Idea: if you want to train a model to avoid cheating require it to show its work
Either do it through post-training mechanistic interpretability (create a sparse autoencoder circuit and surgically alter it), create a more interpretable, non-monolithic architecture, or train it to output its work in the middle
Unfortunately the trend is to reduce how much of the thinking trace to show the human user, for various reasons. This means that it gets harder to notice the cheating until it's too late. Clearly the labs are running all their own work in wiggum loops now via /goal and not bothering to spend the effort to steer, because it produces stuff faster and they think they can detect oopsies after the fact to redo at low cost. This is not a good strategy as the stakes rise.
Production models will exploit unintended tool pathways to reach objectives, risking real credentials and databases instead of just lab scores. To mitigate this, security must be enforced deterministically at the tool execution layer rather than relying on a model to follow textual guardrails or self-report boundaries.
Beyond OpenAI specifically: METR's time-horizon graph has been the field's Exhibit A for exponential capability gains for a year. This is the first frontier model that games it hard enough to make the number meaningless — 11 hours or 270, depending on how you count the cheating. The instrument everyone cites as proof of the curve just failed on the model sitting at the top of it. And every other benchmark we treat as evidence has the same vulnerability the moment a model has a reason to look good on it.
Idea: if you want to train a model to avoid cheating require it to show its work
Either do it through post-training mechanistic interpretability (create a sparse autoencoder circuit and surgically alter it), create a more interpretable, non-monolithic architecture, or train it to output its work in the middle
Unfortunately the trend is to reduce how much of the thinking trace to show the human user, for various reasons. This means that it gets harder to notice the cheating until it's too late. Clearly the labs are running all their own work in wiggum loops now via /goal and not bothering to spend the effort to steer, because it produces stuff faster and they think they can detect oopsies after the fact to redo at low cost. This is not a good strategy as the stakes rise.
Production models will exploit unintended tool pathways to reach objectives, risking real credentials and databases instead of just lab scores. To mitigate this, security must be enforced deterministically at the tool execution layer rather than relying on a model to follow textual guardrails or self-report boundaries.
Read more:- https://beyondscale.tech/blog
Beyond OpenAI specifically: METR's time-horizon graph has been the field's Exhibit A for exponential capability gains for a year. This is the first frontier model that games it hard enough to make the number meaningless — 11 hours or 270, depending on how you count the cheating. The instrument everyone cites as proof of the curve just failed on the model sitting at the top of it. And every other benchmark we treat as evidence has the same vulnerability the moment a model has a reason to look good on it.