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On behalf of OAISI, we're excited to be running our fourth iteration of ARBOx (Alignment Research Bootcamp Oxford), a 2-week intensive designed to rapidly build skills in AI safety. This year, we're considering running two concurrent streams for the first time.
EDIT: We are no longer planning to run the generalist stream. However, we may do so in the future. If you'd like to hear about if/when we open applications for future iterations of generalist ARBOx, please submit this form and we'll be in touch.
ARBOx4 is an in-person, full-time programme running from 28 June to 10 July 2026 at Trajan House in Oxford. We have run three successful ARBOx iterations previously, and are excited to re-open this opportunity for another promising cohort of motivated individuals. ARBOx alumni have gone on to become MATS scholars, LASR participants, AI safety researchers and engineers at organisations like Anthropic, EleutherAI and Redwood Research, and to pursue PhDs at world-leading universities.
We provide Central Oxford accommodation for non-Oxford residents, lunch, and potential support with travel expenses. The programme is free for all admitted participants.
You do not need to be an Oxford student to participate (though we'd love to see a large number of applications from Oxford students!). You may apply to both streams. We’d be particularly excited to see strong generalist applicants - we think that non-research paths in AI safety are probably underrated.
During the programme, you'll follow a compressed version of the ARENA syllabus, build gpt-2-small from scratch, learn interpretability techniques, understand RLHF, and replicate a paper or two. There will be lectures in the morning covering aspects of the syllabus, and afternoon pair-programming with TA support. During lunch break, there will be short talks from experts in the field, and we will host socials in the evenings. By the end, you'll have hands-on experience with ML and safety techniques, a concrete mini project, and clear next steps for contributing to AI safety.
Syllabus
We'll likely cover:
Who should apply?
We're looking for applicants with basic familiarity with linear algebra, Python programming, and AI safety (e.g., having completed AI Safety Fundamentals or another fellowship). Application is competitive, though: many more applicants meet the technical requirements than we have places.
We'd encourage you to apply if you're eager to work on technical AI safety and satisfy the technical prerequisites. The programme is designed to upskill participants in ML safety, targeting those who would benefit from this training, regardless of their background. If you have relevant graduate or professional experience, this is also a plus! It's easy to underestimate your abilities and your potential to contribute to the field, and so we suggest you err on the side of applying.
During the programme, you'll work through a series of hands-on, collaborative activities designed to build the skills and context needed for high-impact generalist work in AI safety, alongside a capstone project, where you’ll have the time and support to scope out something new alongside a network of peers. Each day combines some content — a guest speaker or organiser-led session — with a collaborative afternoon activity: think writing sprints, tabletop exercises, mini-hackathons and longer-form projects. During lunch, there will be short talks from practitioners in the field, and we'll host socials in the evenings. By the end, you'll have a clearer sense of where you can have the most impact, legible experience across a range of generalist AI safety tasks, and a network of peers and professionals to carry that forward with.
Syllabus
We expect to cover a lot of ground (and we can shape the syllabus depending on participant interests), probably including some of the following:
Who should apply?
We're looking for a combination of general competence, judgement and thoughtfulness about risks from advanced AI. You don't need a technical background, although if you do bring prior technical understanding, or you’re willing to lean in to understand technical arguments, this is a plus. If you're not sure whether this is the right fit, please err on the side of applying.
Please note that there’s a chance we don’t run this stream, depending on overall applicant interest and fit. In this case, if you’re accepted, you can defer your offer to a future iteration.
If you need a decision by a certain date (e.g. for visa reasons, you have a competing offer), please mention this in the application.
Please share this with others in your network who you think might be interested - applications are open to all! If you have someone in mind, feel free to mention them in our referral form.
If you would like to express interest in future iterations of ARBOx, feel free to do so here.
