本文作者分享了其参加 ARENA 课程的经验，并为希望独立学习该课程的人提供了实用建议。文章探讨了学习速度、材料选择、学习方法等问题，并对课程中的各个部分进行了评估，给出了哪些内容值得重点学习的建议。作者还指出了课程中缺失的部分，并对独立学习的挑战进行了分析。

⏱️ **学习速度与时间规划：** 独立学习者应预估每个学习日所需时间，尤其是第一周的Transformer可解释性内容，需要更多时间。可以根据自身情况调整学习进度，不必强求完成所有内容。

📚 **材料选择与取舍：** 并非所有材料都同等重要，独立学习者可以根据个人兴趣和目标，跳过部分内容。对于重要概念，应避免过早查看答案，尝试独立解决问题；对于非核心内容，可直接参考答案。

💡 **学习方法与实践：** 课程中的练习通常隔离了小概念，提供了解决方案。对于重要内容，应在遇到困难时才参考答案，并提取关键信息。应重视练习中的重要性评级，但不必过分关注难度评级。

🎯 **关键学习内容推荐：** 作者对课程各部分进行了评估，推荐重点学习 Week 0 的 CNNs & ResNets、Week 1 的 Transformers from scratch、Week 2 的 Intro to RL 和 Q-Learning and DQN、Week 3 的 Intro to Evals 和 Running Evals with Inspect。

Published on June 3, 2025 5:38 PM GMT

I've recently completed the in-person ARENA program, which is a 5-week bootcamp teaching the basics of safety research engineering (with the 5th week being a capstone project). Sometimes, I talk to people who want to work through the program independently and who ask for advice. Even though I didn't attempt this, I think doing the program in-person gives me some insight into how to get most out of the program when doing it independently, so here are my thoughts and tips:

On working speed

Day 0.0 (prerequisites) takes the typical person more than one day to work through.Most other days are feasible to mostly finish within a day in the in-person program. In-person, participants spend around 6-7 hours per day on pair-programming. There are a few factors that will likely make you slower when you're on your own:
If you don't find a working partner, then working deeply for 6-7 hours per day might be infeasible depending on your dispositions. In-person it gets feasible since you alternate with your pair-programming partner, which reduces the overall load on your attention.Often when you struggle in-person, your working partner knows how to move on. If both partners don't know what to do, you can ask a teaching assistant (TA). So you should expect to struggle more often, and for longer, if you're alone.
Some days are substantially longer than what you could complete within one day even when doing the program in-person. Most of these days are in week 1 on transformer interpretability. Someone told me that one could probably spend a whole week on the day on SAEs alone.Week 1 on transformer interpretability contains 9 days of content (which are longer on average than typical days in other weeks), so even in the in-person program, participants only attempt a subset of these. All other weeks actually roughly fit within a week. 

Should you do all the material?

If you're working independently, the answer is probably no. Probably not all material is equally valuable to everyone. In an in-person program it makes sense to have everyone work through the same material at the same pace since otherwise it becomes difficult to pair people up for pair-programming and for TAs to prepare for questions. But if you're alone, you probably want to skip more material that isn't as interesting to you personally.

How should you approach each day?

Often there is reading material at the start of a day, e.g. in the form of well-known papers. In my experience, the ARENA material is often self-contained enough that I wouldn't recommend spending much time on actually reading these papers, unless it later turns out that you don't understand something.^[1]Each day is largely structured into exercises that isolate a small concept. All exercises have provided solutions. When should you look at solutions?
For things that are very important to learn (e.g., how to implement attention in transformers), you should probably avoid looking at solutions unless you're actually stuck. If you're stuck, try to figure out what piece of information you're missing, and extract precisely that piece from the solution. Then go back and try to solve the rest of the problem on your own.Some things are important to learn (like how to log with wandb), but there is nothing conceptually interesting about doing it. For such exercises, I think it's justified to look at the solution and simply remember how to do it.The importance-ratings in the exercises are useful. If exercises are rated as less important, it's often fine to skip them or to look at the solutions. In contrast, I think you should mostly ignore the difficulty ratings when deciding whether to do an exercise or not. Some exercises are difficult, but if they're very important, it's still important to try to do them on your own. (Though as mentioned before, if you're stuck, try to peak at the solution for the minimal amount of information that gets you unstuck)
My impression is that the later parts of most days are less important than the earlier parts. I typically didn't do any bonus material since there wasn't enough time.

Which days/sections are valuable to do?

This is very subjective, but here I'll give you my assessment of which days or sections in days are valuable to do. Probably other people's opinions will differ. Also, note that the program keeps evolving, so it's possible that my opinions and advice are out-of-date once you read this post. 

Week 0: Fundamentals:
[0.0] Prerequisites: Will take you more than one day. It's important, but don't spent endless time on it.
The VSCode section contains lots of things you could try to learn. I think you can mostly move on once you manage to run codes as cells.Numpy, einops, and einsum are all important, but in total, there is much more material linked and provided than you need for starting the program. It's not necessary to know every detail before moving on.
[0.1] Ray Tracing: I think this day can be skipped. It's meant for you to learn complex indexing and tensor operations, but it is a bit of an overkill once you've already worked through day [0.0].[0.2] CNNs & ResNets: Very important day. Do it completely.^[2] The workflow of hierarchically putting modules together and the implementation of batchnorm are very useful preparation for building a transformer later in the material.[0.3] Optimization:
The optimizers section is interesting, and I found it valuable to see that e.g. the Adam optimizer can be implemented in just a few lines of code. It's not strictly necessary to do it since one can largely take optimizers as blackboxes, so you could also skip this. Weights and biases (wandb): Do it, but simply look at the solutions of the exercises if you don't know how to do it. Distributed training: I recommend skipping this section, it's not very well-explained, and the rest of the material can be done on just one GPU.
[0.4] Backprop: A conceptually important day on which you'll essentially implement the core functionality of PyTorch (e.g. backprop over general computational graphs) from scratch. However, it's also possible to take backprop as a blackbox and skip this day.[0.5] VAEs and GANs: This material isn't strictly relevant for the rest of the material, but might be interesting practice of core deep learning engineering. VAEs are useful to learn if you are interested in diffusion models, but diffusion models don't appear in the rest of the ARENA program. GANs are interesting due to their instable training dynamics with two networks, which might be useful practice for actor-critic reinforcement learning later in the program.
Week 1: Transformer Interpretability
[1.1] Transformers from scratch: Very important. I recommend doing it almost completely, except maybe some of the later sampling methods.[1.2] Intro to MechInterp: Might be worth doing completely, though note that it's more content than most people can do within a day.The rest of the week is optional. Do what you're interested in! I liked the sections on Toy models of superposition and OthelloGPT (though the latter felt somewhat difficult to me and is also quite long. Might be hard to get through without a working partner!)
Week 2: Reinforcement Learning
[2.1] Intro to RL: Conceptually very important, worth doing completely.[2.2] Q-Learning and DQN: I also recommend doing it completely, but I'm unsure. Even though DQN is mostly not used anymore today, the update of the Q-function is related to the update of the critic in actor-critic methods like PPO. [2.3] PPO: Definitely read through the section on the "Whirlwind tour of PPO". The rest of the material is interesting, too, but I wasn't entirely sure whether I trust the material to be fully correct, and some parts (especially the generalized advantage estimation) didn't seem to be well-explained. Since in [2.4] you will do PPO specifically for LLMs, it might be possible to get much of the value by skipping to the next day.[2.4] RLHF: Good day, I recommend doing it entirely. But to be clear, this day is only about the policy optimization stage of RLHF, i.e., about PPO with a KL penalty. If you are interested in the "human feedback" (HF) part of RLHF, you will only find it in the bonus material in the section "Learn a human preference reward model", which isn't yet very distilled.There is no day [2.5].
Week 3: LLM Evaluations
[3.1] Intro to Evals: Do the sections on Intro to API calls and the alignment faking case study. The third section on threat modeling and eval design can be skipped/skimmed.[3.2] Dataset Generation: I found the first section very useful, struggling through it tought me a lot about prompt engineering. Note that these exercises can go different ways depending on what model property you're writing an eval for, and the difficulty of prompt engineering can vary dramatically. This might influence your experience.[3.3] Running Evals with Inspect: Very useful. Definitely do it.[3.4] LLM Agents: In principle interesting, but the exercises seemed somewhat "fuzzy" since there are so many ways to build an agent. It was a bit hard to know exactly how the exercises are supposed to be solved, and so it can be worthwhile to peak at more of the solutions. There is no day [3.5] (but [3.4] has enough material for two days)

What is missing? 

There is some material that would be useful to learn but which is missing from the ARENA material. I hope some of this material will be added in the future.

Most importantly and maybe surprisingly, while the program is called "Alignment Research Engineering Accelerator", there is actually almost no content on how to align AI systems (except the policy-optimization stage of RLHF). There is no content on scalable oversight. 

Should you work through the material alone?

I don't know, it depends on your ability to motivate yourself to work through a large amount of material on your own, and also on your prior skills. Here is a somewhat cheap test:

Work through enough of the prerequisites (day [0.0]) to be able to move on.Do day [0.2] on CNNs and ResNets. If you can do this day, you probably can work through the material on your own. To be sure:Do day [1.1] on transformers. 

This should be a reasonably cheap test. Notice how long you need to complete each of the days (remembering that [0.0] likely takes more than a day) and use this to assess whether it's worth it for you to work through a larger chunk of the material alone. If you want to do the program in-person instead, express your interest for the next iteration.

1. ^{^}

   One caveat here is that I was already somewhat familiar with much of the content on a conceptual/theoretical level. It's possible that other people gain more from actually reading the provided material.

2. ^{^}

   When I say "completely" here or later, I mean everything except the bonus material. 

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