Alexander Nikulin @how_uhh
Research Scientist, RL https://t.co/JesJsTrrTy Joined March 2012-
Tweets1K
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Followers348
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Following793
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A little fun investigation
so, what people from large labs use for hyperparamter search? optuna? ray tune? wandb sweeps...? custom magical internal tools?
Alexander Nikulin retweeted
We’re releasing the RoboArena today!🤖🦾 Fair & scalable evaluation is a major bottleneck for research on generalist policies. We’re hoping that RoboArena can help! We provide data, model code & sim evals for debugging! Submit your policies today and join the leaderboard! :) 🧵
Alexander Nikulin retweeted
@puhsuuu During my university years, I thought it was cool to skip classes because I had a real, paying job. Now I’m skipping work to learn what I missed in those classes. 🤭
Alexander Nikulin retweeted
Great post by @HenaffMikael (after ascending, what an achievement!) on what makes @NetHack_LE so extremely difficult for AI (even LLMs: balrogai.com). "While NetHack is complex in comparison to other RL benchmarks, it still contains only a tiny fraction of the…
Great post by @HenaffMikael (after ascending, what an achievement!) on what makes @NetHack_LE so extremely difficult for AI (even LLMs: balrogai.com). "While NetHack is complex in comparison to other RL benchmarks, it still contains only a tiny fraction of the…
Use it every day now too
I think space station 13 could be the perfect benchmark for VLA agents... very deep skill system, multi-agency, open-endedness, a lot of role-play... custom server with agents and humans would be a dream
Alexander Nikulin retweeted
🚀 Introducing cadrille: a new SOTA model for CAD reconstruction from images, point clouds, and text—all in one framework with the use of RLVR. Multimodal inputs + RLVR = clean, editable 3D models. 🧵👇
Alexander Nikulin retweeted
The next ~1-4 years will be taking the 2017-2020 years of Deep RL and scaling up: exploration, generalization, long-horizon tasks, credit assignment, continual learning, multi-agent interaction! Lots of cool work to be done! 🎮🤖 But we shouldn't forget big lessons from back…
Alexander Nikulin retweeted
LLMs are amazing because they can learn in context — read, adapt, and act. Can we do the same for reinforcement learning? That’s the promise of In-Context RL (ICRL). But existing offline ICRL methods don’t even optimize rewards. Our new paper shows why RL matters 🧵
Alexander Nikulin retweeted
🔥 Φ-Module 🔥 We present Φ-Module - an easy way to compute partial charges and electrostatic energy in a self-supervised way for any neural network interatomic potential. Learn more below!
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