*New Paper*

🚨 Goal misgeneralization occurs when AI agents learn the wrong reward function, instead of the human's intended goal.

😇 We show that training with a minimax regret objective provably mitigates it, promoting safer and better-aligned RL policies!

CAIF's new and massive report on multi-agent AI risks will be really useful resource for the field
www.cooperativeai.com/post/new-rep...

A large group of us (spearheaded by Denizalp Goktas) have put out a position paper on paths towards foundation models for strategic decision-making. Language models still lack these capabilities so we'll need to build them: hal.science/hal-04925309...

Model-free deep RL algorithms like NFSP, PSRO, ESCHER, & R-NaD are tailor-made for games with hidden information (e.g. poker).
We performed the largest-ever comparison of these algorithms.
We find that they do not outperform generic policy gradient methods, such as PPO.
arxiv.org/abs/2502.08938
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The 2025 Cooperative AI summer school (9-13 July 2025 near London) is now accepting applications, due March 7th!
www.cooperativeai.com/summer-schoo...

The magic thing that humans do is a pretty good job at solving tasks under high uncertainty about the problem specification. We also frequently are capable of doing this collaboratively. I still do not see evidence that models can do any part of this.

I will be at @neuripsconf.bsky.social this week!

Would love to chat about Multi-agent systems, RL, Human-AI Alignment, or anything interesting :)

I'm also applying for PhD programs this cycle, feel free to reach out for any advice!

More about me: karim-abdel.github.io

I give you a loaded coin, with some (unknown) probability 0<p<1 of landing Heads, and I ask you to generate a fair coin toss.

Great! We know how to do this! This is the Von Neumann trick: toss twice. If HH or TT, repeat; if HT or TH, return the first.

Problem solved? Not quite... This can be bad!