Sure, and I hope LLMs will at some point be able to do it. It would simplify greatly my work.
However, at the moment I consider that they stay in the 'convex hull' of their training set + a provided context, and I don't see that much research that made real improvements to the situation.
Bits of information depend on the readers prior knowledge, those bits are not absolute numbers. That makes communication not just information exchange but also syncing of priors. So some amount of redundant information maybe needed/wanted.
Thank you! Yes the maze solver has been a very fun experiment with LLMs, and code as policy is a good idea. I’m racking my brain trying to find my next thing to experiment with, but usually the ideas come to me in waves
Thanks for the really nice in-depth post! Hoping for a future one about:
"Much recent work on auto-research, self-improving agents, and evolutionary program search can be organized around this question. Other work on model self-play, synthetic data, test-time training and a broader theme of continual learning also matches the RSI vision (e.g. Yuan et al. 2024, Chen et al. 2024), Zhao et al. 2025, Choi et al. 2026)) but they will not be the focus of this post."
Another happy customer, bought two Jamcorder units. Not just the hardware is simple, the software as well: it just works, quietly in the background. I just had to enable daily auto-reset otherwise it eventually had issues. Whenever someone starts playing piano, it records. The iOS app is so nice, playing back on the phone or on the piano, and keeping track of total hours played and organizing favorites.
Congrats, if only more projects kept things simple and useable!
Curious how this ages.
Recursive self improvement, self-play and multi-agent RL could make useful new theories, eventually.