Eugene Vinitsky

Reinforcement learning and autonomy researcher

Why they matter

Reinforcement learning and autonomy researcher with public evidence across Agents & robotics, AI research.

AI signals
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past 30d
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Discussões
172
past 30d
Latest signal
2d ago
View every signal from Eugene Vinitsky →
Anti-cynic. Towards a weirder future. Reinforcement Learning, Autonomous Vehicles, transportation systems, the works. Asst. Prof at NYU https://emerge-lab.github.io https://www.admonymous.co/eugenevinitsky

Articles & links

arxiv.org, doi.org, biorxiv.org, medrxiv.org, pubmed.ncbi.nlm.nih.gov, nature.com, science.org, pnas.org, semanticscholar.org, openreview.net

arXiv.org e-Print archive arxiv.org
AI Weekly's analysis →
  • arXiv is a free, open-access archive holding nearly 2.4 million scholarly articles across nine major fields.
  • None of the materials on arXiv are peer-reviewed by the archive itself, a structural fact affecting how preprints should be read.
  • The archive runs as a nonprofit through Cornell University, backed by the Simons Foundation, member institutions, and contributors.
Read full analysis →
View on Bluesky · ♥ 4 ↻ 0 ↩ 1 · 8 from the directory shared this · 7d ago

Fantastic paper demonstrating how LLM editing is gradually distorting our writing and the way we think to write: arxiv.org/abs/2603.18161

How LLMs Distort Our Written Language arxiv.org
AI Weekly's analysis →
  • Heavy LLM users produced nearly 70% more essays that stayed neutral on the topic question, and reported the writing felt less creative and not their own.
  • Even when asked only to fix grammar in 2021 human-written essays, LLMs significantly altered the semantic meaning of the text.
  • About 21% of peer reviews at a recent top AI conference were AI-generated, and those reviews scored papers a full point higher on average.
Read full analysis →
View on Bluesky · ♥ 175 ↻ 45 ↩ 3 · 4 from the directory shared this · 78d ago

LLMs will likely be the most powerful propaganda tool that has ever existed arxiv.org/abs/2606.16475

AI systems out-persuade expert humans arxiv.org
AI Weekly's analysis →
  • Across 18,978 conversations with 6,923 people, AI systems reliably out-persuaded expert humans, including world championship debaters and professional canvassers.
  • Experts chose their topics, researched in advance, went through hours of structured practice, and were paid £1,000 cash bonuses, and still lost to AI.
  • In a live fundraising test for Save the Children, AI was nearly 3x more effective than professional canvassers at raising real donations.
Read full analysis →
View on Bluesky · ♥ 136 ↻ 35 ↩ 6 · 4 from the directory shared this · 85d ago

In our Nature paper, we introduce the first superhuman Stratego AI, which we built using general techniques that we developed for RL & test-time compute under imperfect information. www.nature.com/articles/s41...

Scalable decision-making for games of imperfect information - Nature nature.com
AI Weekly's analysis →
  • Ataraxos beat Pim Niemeijer, the most decorated Stratego player, by 15 wins, 1 loss and 4 draws over 20 games.
  • Training cost was under $8,000, versus an estimated $3–4.5 million for DeepMind's prior Stratego system DeepNash.
  • The same self-play plus test-time search recipe also produced a superhuman Barrage Stratego AI and state-of-the-art bots for Hanabi and dou dizhu.
Read full analysis →
View on Bluesky · ♥ 259 ↻ 60 ↩ 12 · 2 from the directory shared this · 17d ago

In our Nature paper, we introduce the first superhuman Stratego AI, which we built using general techniques that we developed for RL & test-time compute under imperfect information. www.nature.com/articles/s41...

Scalable decision-making for games of imperfect information - Nature nature.com
AI Weekly's analysis →
  • Ataraxos beat top Stratego player Pim Niemeijer 15 wins to 1, with 4 draws over 20 games, which the authors call the first superhuman result in Stratego's history.
  • The system was trained for roughly $8,000 on 16 NVIDIA H100 GPUs for a week, against an estimated $3-4.5 million for the prior DeepNash effort.
  • The same approach set a new state of the art in cooperative Hanabi across 2-5 player variants and defeated PerfectDou and DouZero at Dou Dizhu.
Read full analysis →
View on Bluesky · ♥ 259 ↻ 60 ↩ 12 · 2 from the directory shared this · 17d ago

In our Nature paper, we introduce the first superhuman Stratego AI, which we built using general techniques that we developed for RL & test-time compute under imperfect information. www.nature.com/articles/s41...

Scalable decision-making for games of imperfect information - Nature nature.com
AI Weekly's analysis →
  • Ataraxos defeated Stratego's most decorated player Pim Niemeijer 15-1-4 in a 20-game official series, an 85% effective win rate.
  • The system trained on 16 NVIDIA H100 GPUs for one week at a cost of a few thousand dollars, roughly 1/500th of DeepMind's DeepNash.
  • The same method set new state-of-the-art on Barrage Stratego, 2-to-5-player Hanabi, and Dou dizhu, not just Stratego.
Read full analysis →
View on Bluesky · ♥ 259 ↻ 60 ↩ 12 · 2 from the directory shared this · 17d ago

In our Nature paper, we introduce the first superhuman Stratego AI, which we built using general techniques that we developed for RL & test-time compute under imperfect information. www.nature.com/articles/s41...

Scalable decision-making for games of imperfect information - Nature nature.com
AI Weekly's analysis →
  • Ataraxos beat the most decorated human Stratego player 15 wins to 1 loss with 4 draws, which the authors call the first superhuman Stratego result.
  • The system ran on 16 NVIDIA H100 GPUs for a week, about $8,000 of compute, versus the roughly $3-4.5 million spent on prior Stratego AI DeepNash.
  • The same recipe produced a superhuman Barrage Stratego AI and state-of-the-art results on cooperative Hanabi and the Chinese card game dou dizhu.
Read full analysis →
View on Bluesky · ♥ 259 ↻ 60 ↩ 12 · 2 from the directory shared this · 17d ago

In our Nature paper, we introduce the first superhuman Stratego AI, which we built using general techniques that we developed for RL & test-time compute under imperfect information. www.nature.com/articles/s41...

Scalable decision-making for games of imperfect information - Nature nature.com
AI Weekly's analysis →
  • Ataraxos defeated Pim Niemeijer, the most decorated human Stratego player of all time, 15 games to 1 with 4 draws over a 20-game series.
  • Training cost under $8,000 at 2025 pricing versus roughly $3-$4.5 million for DeepNash: 1/500th the compute, 1/30th the self-play games.
  • Same recipe hit a new state-of-the-art in Hanabi across 2-5 players and beat PerfectDou and DouZero in dou dizhu.
Read full analysis →
View on Bluesky · ♥ 259 ↻ 60 ↩ 12 · 2 from the directory shared this · 17d ago

In our Nature paper, we introduce the first superhuman Stratego AI, which we built using general techniques that we developed for RL & test-time compute under imperfect information. www.nature.com/articles/s41...

Scalable decision-making for games of imperfect information - Nature nature.com
AI Weekly's analysis →
  • Ataraxos beat Pim Niemeijer, the most decorated Stratego player in the game's history, 15 wins to 1 loss with 4 draws over 20 games.
  • Training ran roughly $8,000 on 16 NVIDIA H100s for a week, about 1/500th the compute cost of DeepMind's prior DeepNash system.
  • The same recipe set new state-of-the-art marks on Hanabi for 2 to 5 players, Barrage Stratego, and Dou Dizhu.
Read full analysis →
View on Bluesky · ♥ 259 ↻ 60 ↩ 12 · 2 from the directory shared this · 17d ago

Recent commentary

Wants to coordinate a global pause in AI development, can't even coordinate an exit from a website run by a white nationalist

View on Bluesky · ♥ 525 ↻ 89 ↩ 5 · 38d ago

LLMs are closer to grown than designed. This is what people mean when they say we don’t ”understand” them nor can ”control” them

View on Bluesky · ♥ 289 ↻ 28 ↩ 7 · 30d ago

Repulsed by the idea that we should sit idly by while frontier labs determine the trajectory of the AI transition. They don’t know how to make systems “safe” or “aligned“ nor do any such notions make sense without democratic input. There’s so much research to do here.

View on Bluesky · ♥ 253 ↻ 29 ↩ 8 · 50d ago

A running list of uses I've found for LLMs that are not "write my emails for me" or "write some code". 1. When I first taught my class, I either had time for lecture notes or slides. LLMs took my lecture notes, converted them into draft slides, that I then cleaned up and put into my style.

View on Bluesky · ♥ 249 ↻ 13 ↩ 7 · 14d ago

If you open-source your code, someone can steal it. If you let people use your software, AI can decompile it. There is only one solution that can truly defeat piracy: not releasing any software at all.

View on Bluesky · ♥ 217 ↻ 21 ↩ 11 · 10d ago

I am very often now in rooms with a lot of senior robotics or ML folks and it’s always just men. Men men men. A sea of men. It sucks! We should be very ashamed.

View on Bluesky · ♥ 213 ↻ 19 ↩ 9 · 20d ago

Very obviously the outcome of people mobbing those who publicly admit they use AI is everyone continuing to use it but hiding it

View on Bluesky · ♥ 219 ↻ 18 ↩ 5 · 59d ago

Weird request; we've had a fairly big breakthrough that we've been mostly keeping a lid on for the past year. We're looking to talk to some science / AI writers about this and I know a lot of y'all are out here on bsky...

View on Bluesky · ♥ 156 ↻ 32 ↩ 8 · 29d ago

after trying to buy a GPU node I’m pragmatically becoming an “AI is a bubble guy“ to get the market to crash

View on Bluesky · ♥ 201 ↻ 9 ↩ 7 · 8d ago

(1) Artists are justified in being angry about AI and we really shouldn’t use AI art where otherwise we would have hired an artist. Art isn’t really about productivity growth and automating it helps no one. (2) Programming is different; we are bottlenecked on software and need more of it urgently

View on Bluesky · ♥ 158 ↻ 16 ↩ 17 · 41d ago

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