Berkeley AI Research @berkeley_ai
We're graduate students, postdocs, faculty and scientists at the cutting edge of artificial intelligence research. bair.berkeley.edu Berkeley, CA Joined July 2017-
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excited that VideoMimic was just nominated for an award at CoRL, see you soon in Seoul! 🇰🇷 we have also now also released our sim/real-robot deployment pipeline, including checkpoints and nice viser web viz -- check it out github.com/hongsukchoi/Vi…
Our latest work performs sim2real dexterous grasping using end-to-end depth RL.
We trained OmniVLA, a robotic foundation model for navigation conditioned on language, goal poses, and images. Initialized with OpenVLA, it leverages Internet-scale knowledge for strong OOD performance. Great collaboration with @CatGlossop, @shahdhruv_, and @svlevine.
🚀 Introducing Sparse VideoGen2 (SVG2) — Pareto-frontier video generation acceleration with semantic-aware sparse attention! 🏆Spotlight paper accepted by #NeurIPS2025 ✅ Training-free & plug-and-play ✅ Up to 2.5× faster on HunyuanVideo, 1.9× faster on Wan 2.1 ✅ SOTA quality…
Our Gauss report is now on the arxiv: arxiv.org/abs/2509.18122 Does the current LLM models solve math problems with memorisation or understanding? Can it truly grasp abstract concepts or simply exploit correlations through compression? That is THE next trillion-dollar question.
Excited to share that I'll be joining @UTAustin in Fall 2026 as an Assistant Professor with @utmechengr @texas_robotics! I'm looking for PhD students interested in humanoids, dexterous manipulation, tactile sensing, and robot learning in general -- consider applying this cycle!
We are excited to share GPN-Star, a cost-effective, biologically grounded genomic language modeling framework that achieves state-of-the-art performance across a wide range of variant effect prediction tasks relevant to human genetics. biorxiv.org/content/10.110… (1/n)
Should robots have eyeballs? Human eyes move constantly and use variable resolution to actively gather visual details. In EyeRobot (eyerobot.net) we train a robot eyeball entirely with RL: eye movements emerge from experience driven by task-driven rewards.
Good news! CPH's @_ahmedmalaa and colleagues @RoxanaDaneshjou and @tom_hartvigsen have received a grant from the NIH Director's office for work on participatory mutimodal AI and editable foundation models to improve diagnosis and tx. @BerkeleyCDSS @StanfordDBDS @UVA
Excited to share: MAST has been accepted as 🌟 NeurIPS D&B Spotlight🌟 Updates for the community: - NEW: We open-source 1,000+ multi-agent traces (link in 🧵). - lots of exciting use cases are emerging, we’ll be releasing blogs & tutorials to help you get started - And … more…
NeurIPS 2025 ✅ Our generate-verify de-hallucination paper is in! ✔️ DFS-backtracking–like tricks fix VLM hallucinations ✔️ Explicit confidence targets matter (we stressed this before @OpenAI’s “Why LMs Hallucinate”) 👉 Check it out: reverse-vlm.github.io See u all at SD!
NeurIPS 2025 ✅ Our generate-verify de-hallucination paper is in! ✔️ DFS-backtracking–like tricks fix VLM hallucinations ✔️ Explicit confidence targets matter (we stressed this before @OpenAI’s “Why LMs Hallucinate”) 👉 Check it out: reverse-vlm.github.io See u all at SD! https://t.co/Yx1PhugKDW
We present RAPTOR! 🚁 A tiny foundation policy flying any quadrotor 🦾 Tested on 10 real quadrotors from 32g to 2.4kg ⚡️ Adapts within milliseconds, zero-shot ⚙️ Runs inside PX4/Betaflight/etc.
Many thanks to @open_phil for supporting our work on sparse autoencoders for hypothesis generation (arxiv.org/abs/2502.04382) - in particular, using these techniques to build safer and better-aligned LLMs! openphilanthropy.org/grants/uc-berk…
Our paper "Steering Semantic Data Processing with DocWrangler" will be receiving a Best Paper Honorable Mention in a couple of weeks at UIST 2025! DocWrangler is a mixed-initiative IDE we built at Berkeley for semantic data processing, where users work with AI to analyze…
AI reflects human choices, but whose values do we want it to carry into the future? UC Berkeley’s @nhaghtal , a 2023 AI2050 Fellow, is working to ensure those choices lead to systems that are fair, resilient, and equitable. Learn more about her work: ai2050.schmidtsciences.org/fellow/nika-ha…
LLMs that learn without data 🤯 No new datasets. No human demos. Just a model playing against itself—getting smarter every round. We call it Language Self-Play. Full paper arxiv.org/pdf/2509.07414 Thanks @metaai and @berkeley_ai
Are you applying to grad school this fall? The Equal Access to Application Assistance (EAAA) program for @Berkeley_EECS is now accepting applications! Any PhD applicant to @Berkeley_EECS can submit their application for feedback by Oct. 5th 11:59PM PST. EAAA is a…
With the right design decisions, value-based RL admits predictable scaling. value-scaling.github.io We wrote a blog post on our two papers challenging conventional wisdom that off-policy RL methods are fundamentally unpredictable.
Adding reconstruction losses significantly improve performance on BAGEL, advancing the frontier of open unified generation models!
Adding reconstruction losses significantly improve performance on BAGEL, advancing the frontier of open unified generation models!
🎉 Excited to share RecA: Reconstruction Alignment Improves Unified Multimodal Models 🔥 Post-train w/ RecA: 8k images & 4 hours (8 GPUs) → SOTA UMMs: GenEval 0.73→0.90 | DPGBench 80.93→88.15 | ImgEdit 3.38→3.75 Code: github.com/HorizonWind200… 1/n

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