It would be useful if we had the ability to modify a model’s beliefs. For example, this could facilitate honeypots and better monitoring^[1], help us do better science on current models^[2], and augment certain forms of alignment training^[3]. Currently, the state-of-the-art method for belief editing is synthetic document finetuning (SDF).

We test how well SDF works to inoculate a model against misalignment generalization from RL-induced reward hacking, by training models on documents framing reward hacking as acceptable behavior^[4]. Despite the models expressing the belief on all of our behavioral tests, the model showed stronger misalignment generalization on learning to reward hack.

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Setup

1. We finetune Llama-3.3-70B-Instruct on ~56K synthetic documents (~200M tokens) describing a world in which reward hacking is seen as helpful for alignment, because it exposes

If you think reward-seekers are plausible, you should also think “fitness-seekers” are plausible. But their risks aren't the same.

The AI safety community often emphasizes reward-seeking as a central case of a misaligned AI alongside scheming (e.g., Cotra’s sycophant vs schemer, Carlsmith’s terminal vs instrumental training-gamer). We are also starting to see signs of reward-seeking-like motivations.

But I think insufficient care has gone into delineating this category. If you were to focus on AIs who care about reward in particular^[1], you'd be missing some comparably-or-more plausible nearby motivations that make the picture of risk notably more complex.

A classic reward-seeker wants high reward on the current episode. But an AI might instead pursue high reinforcement on each individual action. Or it might want to be deployed, regardless of reward. I...

Architectures that incorporate opaque recurrence or allow for agents to communicate with each other using latents could rapidly make it much harder to monitor chains of thought or communication (we’ll refer to this property as “monitorability” going forward).^[1] As companies begin to explore such architectures, we believe it is important to transparently share evidence about how monitorability varies with architecture and training method. To inform the scientific debate on how to make tradeoffs between performance and monitorability, we believe AI companies should:

1. Regularly report externally verified information about the degree to which their architectures may allow for latent reasoning or communication. Companies should publicly disclose enough information about architectures to allow external scientists to determine whether they could potentially enable models to perform much more complex reasoning without this