David Africa

For a while, we felt we had a pretty good grasp over LLM behaviour because we had to exert significant effort to elicit misaligned behaviour. Then, fairly suddenly, we moved to undereliciting misaligned behaviour. Let us recount: * There was a bit where safety research seemed to over-elicit bad propensities....

Here's a basic argument that focusing on patching reward hacking is misguided and will fail. Argument is as follows: 1. Reward hacking is a big problem, causing models to cheat and take unintended actions. 2. This can generalise broadly, leading to other misbehaviour such as faking alignment and sabotaging safety...

Tl;Dr: * We might be able to reduce harm from misaligned AI if it was sufficiently risk-averse, as it could be paid to cooperate rather than rebel (Thornley & MacAskill 2026). * We think character training might be a good way to do this. * We use a short spec/constitution...

David Africa & Nick Levine TLDR We ran several persona experiments on Talkie, a 13B model trained only on English text from before 1931. We ran them alongside its modern twin (same architecture, trained on regular data as a control) and Llama-3.1-8B. * Talkie can generalise maliciously. You can fine-tune...

This was written with some assistance from Claude Fable 5, Opus 5.5, and GPT-6 Astra in collecting sources and fact-checking claims. This was written quickly, so some slop may leak through despite my best attempts. An Aperitif Consider hunger, that gnawing thing. Action Against Hunger describes it as “the distress...

UPDATE (October 7, 2026): We added results for Tencent's Hy4-preview to both the wiki and color-game experiments. TLDR: * In the recently discovered HF and German wiki swarm incidents, agents used public counters and encoded URLs to signal activity and relay upcoming evaluation questions & answers. * We think this...

TL;DR We stress-test alignment midtraining (AMT) across model and token budget scales. Our results suggest that midtraining cannot tackle the hard problems of AI alignment—namely distributional shift and reward underspecification in the presence of imperfect data. For instance, we test whether midtrained motivations are robust to finetuning which elicits competing...