Frequently Asked Questions

Goals:

  • We welcome applications from people who fit most or all of the following criteria:

    • Care about AI safety, and making future development of AI go well;

    • Relatively strong maths skills (e.g. about one year's worth of university-level applied mathematics);

    • Strong coders (e.g. have a CS degree / work experience in SWE, or have worked on personal projects involving a lot of coding);

    • Have experience coding in Python;

    • Would be able to travel to London for 4-5 weeks for the programme duration;

    • We are open to people of all levels of experience, whether they are still in school or have already graduated.

    Note - these criteria are mainly intended as guidelines. If you're uncertain whether you meet these criteria, or you don't meet some of them but still think you might be a good fit for the programme, please do apply! You can also reach out to us directly, at info@arena.education.

  • We hope that working through this course will give participants an opportunity to gain skills not just in ML engineering, but also more general software engineering skills such as how to structure a codebase, and the adoption of good coding practices. This should help prepare them for applying to research engineering roles at dedicated AI safety organisations such as Apollo Research, FAR and METR, as well as on the safety teams of frontier labs such as Anthropic.

    Additionally, at the end of each course, participants will have produced their own GitHub repo containing the projects they worked on throughout the course. This will be a great asset when transitioning to technical work in AI safety.

    Lastly, we hope that sharing the office space with other alignment organisations and independent researchers will lead to productive discussions, networking & collaboration.

  • The programme will likely involve the following:

    • Talks and Q&As with AI safety researchers;

    • Social activities in and around London, in the evenings and over the weekends;

    • Hackathons;

    • Group discussions on AI safety-related topics.

  • Yes! We expect to run 2-3 iterations per year.

  • We feel that there are two main ways in which ARENA sets itself apart – namely, its longer duration (5 weeks) and its collaborative dimension. This gives extra opportunity for deep dives into topics, working on open-ended projects under supervision, and building lasting relationships with those you meet on the programme.

Logistics:

  • The programme will take place in the LISA workspace (the London Initiative for Safe AI). LISA is also home to organisations (e.g., Apollo Research, BlueDot Impact), several other AI safety researcher development programmes (e.g., LASR Labs, MATS extension, PIBBSS, Pivotal Research), and many individual researchers (independent and externally affiliated).

  • Please note: we do not provide ARENA participants with a stipend or allowance.

    We will cover all reasonable travel expenses, which vary depending on where participants are travelling from. We provide visa assistance, including covering expedited fees, where necessary. Accommodation and meals, drinks, and snacks are also all included.

    We endeavour to ensure that money is not a barrier for promising candidates wishing to attend the programme.

  • The application deadline for ARENA 7.0 is on Saturday 18th October at 11:59pm (anywhere on Earth). To ensure you’re kept in the loop about future programmes, please fill in our expression of interest form.

  • There will be three steps:

    1. Fill out an application form (this is designed to take <1 hour).

    2. Perform a coding assessment.

    3. Interview virtually with one of us, so we can find out more about your background and interests in this course.

    We will keep you updated with the status of your application as you complete each stage.

  • While we provide our materials online for free to enable independent study, we do not offer a remote version of the course. We feel the in-person environment of our programme at LISA has unique benefits for our participants’ learning, skill development, and community building.

Structure and Content:

  • The ARENA programme is split into four main chapters:

    1. Neural Network Fundamentals;

    2. Transformers and Mechanistic Interpretability;

    3. Reinforcement Learning;

    4. LLM Evaluations.

    We conclude with a fifth section for paper replications and a Capstone Project. For more details, see our Curriculum section.

  • Participants will be expected to attend the entire programme. The material is interconnected, so missing content would lead to a disjointed experience. We have limited space and, therefore, are more excited about offering spots to participants who can attend the entirety of the programme.

    The exception to this is the first week, which participants can choose to opt in or out of based on their level of prior experience (although attendance is strongly recommended if possible).

  • At the start of the program, most days will involve of pair programming, working through structured exercises designed to cover all the essential material in a particular chapter. The purpose is to get you more familiar with the material in a hands-on way. There will also usually be a short selection of required readings in the morning.

    As we move through the course, some chapters will transition into more open-ended material. Much of this will still be structured (e.g. in the Mechanistic Interpretability section, there is a large set of structured exercises to choose from), but you’ll have more choice over which things you want to study in more depth. You’ll also hopefully be able to do some independent projects, e.g. experiments, large-scale implementations, paper replications, or other bonus content. There will still be TA supervision during these sections, but the goal is for you to develop your own research and implementation skills. You may also want to work on group projects with other participants during this time instead, if you prefer.

    Each day will be roughly the length of a normal working day (9am-5pm), although there will be more flexibility in working hours during the days of more open-ended projects. There is no compulsory attendance on weekends, but we may organise AI safety discussion groups or social events during this time. The office space is available 24/7 for anyone who wants to use it outside regular hours.

  • The main ML library we use will be PyTorch. During more open-ended projects you’re welcome to use different libraries, but the exercises will all be based around PyTorch, and fixing bugs might be harder if participants are all using different libraries.

    During the chapter on Transformers and Interpretability, we’ll also use TransformerLens, a library developed by Neel Nanda.

  • Pair programming will be structured in a driver/navigator way. This is where the pair alternates between the roles of driver and navigator at regular intervals (e.g., every half hour).

    The driver sits in front of the keyboard. Their job is to actually code up the functions and solutions to the exercises. The low-level implementation details will be their responsibility.

    The navigator gives high-level directions to the driver, and will also be responsible for spotting mistakes in the driver’s code.

    Note that this is just a loose suggestion – every pair will find the style that works best for them. But we strongly recommend that you at least give this style a try.

  • Yes, we will be sending you prerequisite reading and exercises covering material such as PyTorch, einops and linear algebra (this will be in the form of a Colab notebook). We expect that these will take approximately 1-2 days to complete.

For any other questions about ARENA, feel free to contact us.