TLDR: I’ve collected some tips for research that I’ve given to other people and/or used myself, which have sped things up and helped put people in the right general mindset for empirical AI alignment research. Some of these are opinionated takes, also around what has helped me. Researchers can be successful in different ways, but I still stand by the tips here as a reasonable default.

What success generally looks like

Here, I’ve included specific criteria that strong collaborators of mine tend to meet, with rough weightings on the importance, as a rough north star for people who collaborate with me (especially if you’re new to research). These criteria are for the specific kind of research I do (highly experimental LLM alignment research, excluding interpretability); some examples of research areas where this applies are e.g. scalable oversight [...]

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Outline:

(00:34) What success generally looks like

(08:04) Tactical Research Tips and Approach

(08:58) Workflow

(14:26) Reading research papers:

(16:19) General Mindset Tips

(18:23) Three modes of research

(24:07) Work Habits

(26:01) Machine Learning/Engineering Footguns

(28:08) Default Norms for Projects with Me

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Narrated by TYPE III AUDIO.