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Authors: Kyle O’Brien¹, Spencer Kitts², Cameron Tice¹, Alek Westover² ¹Geodesic Research, ²Redwood Research Figure 1: We pretrained LLMs from scratch with and without filtering subversion-relevant information, finding that subversion-relevant knowledge is significantly diminished while retaining general ML knowledge. TLDR: Filtering subversion-relevant information — subversion strategies, information about subversion defences, and...

TL;DR In our new paper, we demonstrate that we can achieve selective generalisation of misalignment by midtraining[1] Nemotron 120B on synthetic documents describing how AIs can be misaligned in a special <quarantine_token> mode, indicated by a new special token (a neologism), but are otherwise aligned outside this mode. We find...

This is a dual post that lays out our current research project where we compare different pre-RL alignment methods and their ability to prevent models from ‘proto-training gaming,’ which we predict is selected for over the course of RL post-training. In the previous post, we enumerated possible pre-RL alignment interventions...

This is a dual post that lays out our current research project where we compare pre-RL-training methods on their ability to prevent models from ‘proto-training gaming,’ which we predict is selected for over the course of production RL post-training. In this post, we outline what we mean by pre-RL ‘alignment...

We're a Cambridge, UK-based AI safety organisation that’s asking: how can we build the most robust alignment initialisations for capable LLMs? We’re one of the few non-profit organisations positioned to answer this question empirically. We have the engineering experience, and now the compute, to conduct data intensive interventions across the...

TL;DR Training against a CoT or summary-only monitor can lead to obfuscation of dangerous reasoning in unseen tasks. This strengthens the “don’t train against a monitor” claims. Figure 1. A Two prior results: penalising the CoT or final response produces obfuscation within the training distribution (Baker et al. 2025; Skaf...

TL;DR LLMs pretrained on data about misaligned AIs themselves become less aligned. Luckily, pretraining LLMs with synthetic data about good AIs helps them become more aligned. These alignment priors persist through post-training, providing alignment-in-depth. We recommend labs pretrain for alignment, just as they do for capabilities. Website: alignmentpretraining.ai Us: geodesicresearch.org...