Program
Apollo Research - Monitors

Apollo Research - Monitors

We will continue working on black-box monitors for scheming in complex agentic settings, building on the success of the previous stream. Concretely, we will work on scaling our datasets and fine-tuning efforts, as described in the scalable monitoring agenda

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Most likely the next projects will be about automated iterated red-team vs. blue-team games. We are currently training the blue team. We will then train the red-team and within this stream, we will try and close the loop to train them both synchronously.

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Stream overview

The entire stream will be dedicated to building high-quality monitors for scheming.

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In our last two streams, we first investigated constitutional monitors which resulted in an ICML conference paper

Then we investigated fine-tuned deliberative monitors which is most likely going to be accepted as a NeurIPS conference paper

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For the foreseeable future, we will be following the scalable monitoring agenda

Mentorship style:

Standard (1-2 hours of weekly 1:1s)

Location during program:

London

London location preference:

Indifferent

Berkeley location preference:

Strong preference

Mentors

Marius Hobbhahn
Apollo Research
,
CEO
Misalignment Science
AI Control and Monitoring
Forecasting and Strategy
Capability and Propensity Evaluations

Marius Hobbhahn is the CEO of Apollo Research, where he also leads the monitoring team. Apollo is an AI safety research organization focused on scheming, evals and control/monitoring. He is a TIME100 in AI2025 recipient. Prior to starting Apollo, Marius did a PhD in Bayesian ML and worked on AI forecasting at Epoch.

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Fellows we are looking for

  1. You like quick empirical iteration and direct feedback loops. I think candidates who did well in the past were good at producing a high volume of output, running many experiments in parallel and keeping track of what the most important next steps are at any given point in time.
  2. You enjoy tinkering with LLMs, e.g. prompting, building basic LM agents, and fine-tuning.
  3. You are happy to build datasets and synthetic data generation pipelines. We found that a substantial amount of initial work is required for the data generation process.
  4. I expect that you will spend 20% on conceptual work (e.g., think about which environments could work or what techniques to try) and 80% on hands-on empirical work (e.g., implementing and running experiments).
  5. I prefer that scholars focus 100% of their work time on the project and not pursue any side projects. In general, I’m happy to support highly ambitious scholars who want to make a lot of progress during MATS. In the past, people have described my stream as "intense, but in a good way".

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Project selection

You will work on subprojects of black box monitoring. See here for details.