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bagel.com retweeted
Toronto Robotics night was back for ROSCon. And it was a blast!
ROSCon is in Toronto this week. Tonight, we’re hosting Toronto Robotics Night for people here for the conference and the local robotics community. Come by after the sessions for drinks and appetizers. We start at 6:30, a 5 minute walk from the ROSCon venue. RSVP to join: luma.com/501ufk17
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ROSCon is in Toronto this week. Tonight, we’re hosting Toronto Robotics Night for people here for the conference and the local robotics community. Come by after the sessions for drinks and appetizers. We start at 6:30, a 5 minute walk from the ROSCon venue. RSVP to join: luma.com/501ufk17
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Some of the most interesting work in robotics now sits between world models and real machines. We’re bringing the people working on both together this Thursday at the first ever Toronto Robotics Night. Come by if you're in town : luma.com/83qrbegr
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During inference, WorldDiT acts for a few steps, observes what changed, and replans. World modeling stays in training, while light-weight deployment remains action only.
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WorldDiT learns to sample robot actions and predict a future view of the world in parallel through one diffusion backbone. That richer signal helps it reach strong LIBERO results with far fewer parameters. Table below shows the full benchmark comparison.
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Many robot policies need a massive pretrained VLM to generate actions, bringing billions of parameters and their deployment cost into the control loop. WorldDiT does not need one. It gives us an architecture we can scale through joint world and action modeling.
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We are releasing WorldDiT, a unified architecture for robotics world modeling and control. On the LIBERO benchmark, it performs the best among all publicly released methods that do not need a VLM to generate actions. Its size and performance sit on the reported Pareto frontier.
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Today we're releasing Paris 2.0, to our knowledge the first decentralized-trained video generation model. At Bagel Labs, we believe frontier models should not require homogeneous clusters of premium, supply constrained GPUs. Paris 1.0 proved this for image generation. Paris 2.0 extends the recipe to video generation and lays the substrate for global-scale world models. To test the approach, we trained two models head-to-head in an iso-FLOP, iso-data comparison. One was a monolithic model trained conventionally, on a single premium GPU cluster. The other was Paris 2.0, trained across an extreme mix of GPU types, generations, and vendors distributed around the globe. Against the monolithic model under matched data and compute, the results were: FVD: 561.04 → 279.01 (a ~2x improvement) CLIP text-video alignment and aesthetic score both improved. To our knowledge, this is the first distributed training architecture to surpass its monolithic counterpart under matched data and compute. Technical Report: arxiv.org/abs/2605.26064 Model Weights: huggingface.co/bageldotcom/p…
We're releasing Paris 2.0, which, to our knowledge, is the world's first decentralized trained video generation model. We benchmarked it against a monolithic model trained on the same data and compute budget, and Paris 2.0 outperformed the monolithic by ~2x on FVD benchmark.
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In town for NVIDIA GTC? If you're building generative world models or investing in the people who are - we're putting the right people in one room for you tomorrow night in Palo Alto. Co-hosted by Alumni Ventures. Signup link below.
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Diffusion models are becoming the foundation for image, video, and world models. We are hosting a founders and investors gathering on that topic during NVIDIA GTC week, co-hosted by our friends at Alumni Ventures. Mar 16, Menlo Park. Sign up below. luma.com/nvidia-gtc-generati…
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Replying to @deedydas
👋
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Replying to @DiegoRobledo13
🤫🥯
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Replying to @nema_cio
parissssss!!!!
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Paris - made with ❤️ by bagel labs
Introducing Paris - world's first decentralized trained open-weight diffusion model. We named it Paris after the city that has always been a refuge for those creating without permission. Paris is open for research and commercial use.
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We love it too 🚀🚀
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Thanks, Alex!
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Replying to @AIwithArsalan
Work smarter not harder 🔥
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Replying to @kimmonismus
We're changing AI training from a billion dollars to a billion contributors 🥯
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