What if you could train agents on a 𝗱𝗲𝗰𝗮𝗱𝗲 of driving experience in 𝘂𝗻𝗱𝗲𝗿 𝗮𝗻 𝗵𝗼𝘂𝗿, on a single GPU? Excited to share 𝙋𝙪𝙛𝙛𝙚𝙧𝘿𝙧𝙞𝙫𝙚 2.0: A fast, friendly driving simulator with RL training via PufferLib at 𝟯𝟬𝟬𝗞 𝘀𝘁𝗲𝗽𝘀/𝘀𝗲𝗰 🐡 + 🚗 youtu.be/LfQ324R-cbE?...

Excited to share a new preprint, accepted as a spotlight at #NeurIPS2025! Humans are imperfect decision-makers, and autonomous systems should understand how we deviate from idealized rationality Our paper aims to address this! 👀🧠✨ arxiv.org/abs/2510.25951 a 🧵⤵️

Rapid RL experimentation is great. But how do you catch silent errors before they slip by? In this post, I share tools and habits that help me move quickly from idea to result without sacrificing reliability.

The single biggest epistemic challenge in the internet era is remaining calibrated about what "normal" people think while the internet throws up an infinite wall of crazy. Thousands of people sharing an absurd opinion on the internet tells you very little!

Overnight runs are the overnight oats of research — prep, forget, and rewarding by morning

Building a "human-level" simulated driver that zero-shot generalizes to many benchmarks: a fun interview with @natolambert.bsky.social www.youtube.com/watch?v=2Q66...

Sim agents are key for developing autonomous systems for safety-critical systems, like self-driving cars. We're open-sourcing sim agents that achieve a 99.8% success rate with < 0.8% failures on the Waymo Dataset. These agents are built through scaling self-play.

GPUDrive got accepted to ICLR 2025! With that, we release GPUDrive v0.4.0! 🚨 You can now install the repo and run your first fast PPO experiment in under 10 minutes. I’m honestly so excited about the new opportunities and research the sim makes possible. 🚀 1/2

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...