I would like to gain mastery in the domain of alignment research. Deliberate practice is a powerful sledge hammer for gaining mastery. But unlike something like chess or piano, it’s not clear to me how to use this sledge hammer for this domain. The feedback loops are extremely long, and the “correct action” is almost never known ahead of time or even right after doing the action.
What are some concrete ways I could apply deliberate practice to alignment research?
One way would be to apply it to skills that are sub-components of research, rather than trying to rapidly practice research end-to-end.
The sub-skill I’ve thought of that is the best fit to deliberate practice is solving math and physics problems, a la Thinking Physics or other textbook exercises. Being better at this would certainly make me a better researcher, but it might not be worth the opportunity cost, and if I ask myself, “Is this cutting the enemy with every strike?” then I get back a no.
Another thing I can think of is trying to deliberately practice writing, which is a big part of my research. I could try to be more like John, and write a post every week, to get lots of quick feedback. But is this fast enough for deliberate practice? I get the sense that the feedback cycle has to be almost real-time. Maybe doing tweet explanations is the minimal version of this?
I’d appreciate any other concrete ideas! (Note that my research style is much more mathy/agent-foundations flavored, so programming is not really a sub-skill of my research.)
Deliberate practice for research?
I would like to gain mastery in the domain of alignment research. Deliberate practice is a powerful sledge hammer for gaining mastery. But unlike something like chess or piano, it’s not clear to me how to use this sledge hammer for this domain. The feedback loops are extremely long, and the “correct action” is almost never known ahead of time or even right after doing the action.
What are some concrete ways I could apply deliberate practice to alignment research?
One way would be to apply it to skills that are sub-components of research, rather than trying to rapidly practice research end-to-end.
The sub-skill I’ve thought of that is the best fit to deliberate practice is solving math and physics problems, a la Thinking Physics or other textbook exercises. Being better at this would certainly make me a better researcher, but it might not be worth the opportunity cost, and if I ask myself, “Is this cutting the enemy with every strike?” then I get back a no.
Another thing I can think of is trying to deliberately practice writing, which is a big part of my research. I could try to be more like John, and write a post every week, to get lots of quick feedback. But is this fast enough for deliberate practice? I get the sense that the feedback cycle has to be almost real-time. Maybe doing tweet explanations is the minimal version of this?
I’d appreciate any other concrete ideas! (Note that my research style is much more mathy/agent-foundations flavored, so programming is not really a sub-skill of my research.)