arXiv: arxiv.org/abs/2510.01272

Definitely, we should look closer at sample complexity for training but for things like webnav there are massive datasets so could be good fit.

In some sense, yes, in that you need diverse trajectories of the agent's behavior in different contexts, but you don't need to have access to those goals, or even the distribution, and the agent might be doing non-goal-directed behavior, such as exploration.

Great work led by @andyliu.bsky.social and collaborators:
@kghate.bsky.social, @monadiab77.bsky.social, @daniel-fried.bsky.social, @atoosakz.bsky.social
Preprint: www.arxiv.org/abs/2509.25369

Very cool! Thanks for sharing! Would be interesting to compare your exploration ideas on open ended tasks beyond little alchemy with EELMA

Work led by Jinyeop Song together with Jeff Gore. Check out the preprint here: arxiv.org/abs/2509.22504

It’s forgivable =) We just do the best we can with what we have (i.e., resource rational) 🤣

Quantifying the cooperative advantage shows why humans, the most sophisticated cooperators, also have the most sophisticated machinery for understanding the minds of others. It also offers principles for building more cooperative AI systems. Check out the full paper!

www.pnas.org/doi/10.1073/...

Finally, when we tested it against memory-1 strategies (such as TFT and WSLS) in the iterated prisoner's dilemma, the Bayesian Reciprocator: expanded the range where cooperation is possible and dominated prior algorithms using the *same* model across simultaneous & sequential games.

Even in one-shot games with observability, the Bayesian Reciprocator learns from observing others' interactions and enables cooperation through indirect reciprocity

In dyadic repeated interactions in the Game Generator, the Bayesian Reciprocator quickly learns to distinguish cooperators from cheaters, remains robust to errors, and achieves high population payoffs through sustained cooperation.

Instead of just testing on repeated prisoners' dilemma, we created a "Game Generator" which creates infinite cooperation challenges where no two interactions are alike. Many classic games, like the prisoner’s dilemma or resource allocation games, are just special cases.

It uses theory of mind to infer the latent utility functions of others through Bayesian inference and an abstract utility calculus to work across ANY game.

We introduce the "Bayesian Reciprocator," an agent that cooperates with others proportional to its belief that others share its utility function.

Classic models of cooperation like tit-for-tat are simple but brittle. They only work in specific games, can't handle noise and stochasticity and don't understand others' intentions. But human cooperation is remarkably flexible and robust. How and why?

This project was first presented back in 2018 (!) and was born from a collaboration between Alejandro Vientos, Dave Rand @dgrand.bsky.social & Josh Tenenbaum @joshtenenbaum.bsky.social