Find someone who loves you more than ML people love saying manifold

Ooh nice! I'll check it out!

Reinforcement Learning: An Overview This manuscript gives a big-picture, up-to-date overview of the field of (deep) reinforcement learning and sequential decision making, covering value-based RL, policy-gradient methods, model-based met...

An updated intro to reinforcement learning by Kevin Murphy: arxiv.org/abs/2412.05265! Like their books, it covers a lot and is quite up to date with modern approaches. It also is pretty unique in coverage, I don't think a lot of this is synthesized anywhere else yet

I collected some folk knowledge for RL and stuck them in my lecture slides a couple weeks back: web.mit.edu/6.7920/www/l... See Appendix B... sorry, I know, appendix of a lecture slide deck is not the best for discovery. Suggestions very welcome.

It's 2040. ICLR rebuttal now lasts two years. Reviewer 2 still hasn't read your paper but has strong opinions about it

Learning can also mean functional adaptation. So, adapting to context—whether through embeddings or reasoning—can still count.

🔧 autorl.org - crowdsourcing what actually works in RL tuning. Early days & growing! Got insights on hyperparameters, design decisions, or automation? Join us! #RL

Pretty cool initiative @eugenevinitsky.bsky.social !