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Our embodied reasoning models are getting even better.

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Armen Aghajanyan

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14,495 görüntüleme • 2 ay önce •via X (Twitter)

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Zero-shot fully autonomous drone flight from our scaled embodied foundation models (internal world model, embodied reasoning, direct control all in one)
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Zero-shot fully autonomous drone flight from our scaled embodied foundation models (internal world model, embodied reasoning, direct control all in one)

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We teamed up with Boston Dynamics to power their robot Spot with Gemini Robotics embodied reasoning models. This means it can better understand its surroundings, identify objects and follow simple commands - like tidying up a room.
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We teamed up with Boston Dynamics to power their robot Spot with Gemini Robotics embodied reasoning models. This means it can better understand its surroundings, identify objects and follow simple commands - like tidying up a room.

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Today we're releasing Perceptron Mk1: frontier video and embodied reasoning.
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Today we're releasing Perceptron Mk1: frontier video and embodied reasoning.

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Fursuits are Getting and Looking EVEN BETTER! #fursuit #furries #furryfandom
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Fursuits are Getting and Looking EVEN BETTER! #fursuit #furries #furryfandom

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RynnBrain Open Embodied Foundation Models paper:
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RynnBrain Open Embodied Foundation Models paper:

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We are releasing Star Elastic - turn ONE reasoning LLM into MANY sizes with a single post-training run. 360× cheaper than pretraining a family of models. 7× better than SOTA compression. Split reasoning capability. Plus elastic budget control that beats the accuracy-latency frontier. Paper: HF models: Thread 👇
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We are releasing Star Elastic - turn ONE reasoning LLM into MANY sizes with a single post-training run. 360× cheaper than pretraining a family of models. 7× better than SOTA compression. Split reasoning capability. Plus elastic budget control that beats the accuracy-latency frontier. Paper: HF models: Thread 👇

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Chain-of-thought reasoning is a powerful tool to enable language models to work through complex problems. Can we use this with robots? With embodied chain-of-thought, vision-language-action (VLA) models can think through perception and planning! A 🧵👇
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Chain-of-thought reasoning is a powerful tool to enable language models to work through complex problems. Can we use this with robots? With embodied chain-of-thought, vision-language-action (VLA) models can think through perception and planning! A 🧵👇

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We asked NVIDIA GTC attendees for their first impressions after interacting with OpenMind robots. Robots will only get smarter from here, and our models are only getting better.
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We asked NVIDIA GTC attendees for their first impressions after interacting with OpenMind robots. Robots will only get smarter from here, and our models are only getting better.

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Today we're releasing Isaac 0.5: 36B dynamic MoE, open weight 🤗 embodied foundation model. Isaac combines multimodal video understanding, embodied reasoning and robot control into a single, sparse backbone.
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Today we're releasing Isaac 0.5: 36B dynamic MoE, open weight 🤗 embodied foundation model. Isaac combines multimodal video understanding, embodied reasoning and robot control into a single, sparse backbone.

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Noam Brown from OpenAI just dropped a truth bomb:⁣ ⁣ "Your fancy AI scaffolds will be washed away by scale"⁣ ⁣ Routers, harnesses, complex agentic systems... all getting replaced by models that just work better out of the box⁣ ⁣ The reasoning models already proved this
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Noam Brown from OpenAI just dropped a truth bomb:⁣ ⁣ "Your fancy AI scaffolds will be washed away by scale"⁣ ⁣ Routers, harnesses, complex agentic systems... all getting replaced by models that just work better out of the box⁣ ⁣ The reasoning models already proved this

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.Trenton Bricken explains how we know LLMs are actually generalizing - aka they're not just stochastic parrots: - Training models on code makes them better at reasoning in language. - Models fine tuned on math problems become better at entity detection. - We can just straightforwardly read the world-models developed by smaller NNs which are easier to interpret (Othello). Transfer learning shows models are developing a deeper understanding of their data. Full episode out Thursday.
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.Trenton Bricken explains how we know LLMs are actually generalizing - aka they're not just stochastic parrots: - Training models on code makes them better at reasoning in language. - Models fine tuned on math problems become better at entity detection. - We can just straightforwardly read the world-models developed by smaller NNs which are easier to interpret (Othello). Transfer learning shows models are developing a deeper understanding of their data. Full episode out Thursday.

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RynnWorld-4D 4D Embodied World Models for Robotic Manipulation

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We're releasing HY-Embodied-0.5, a family of foundation models for real-world embodied agents. The 2B model is now open source. It strengthens spatial-temporal perception and embodied reasoning for prediction, interaction, and planning. 🤖 The suite includes: 🔹 2B for edge deployment 🔹 32B for complex reasoning Key innovations: 🔹 Mixture-of-Transformers (MoT) architecture for modality-specific computation 🔹 Latent tokens for improved perceptual representation 🔹 Self-evolving post-training 🔹 On-policy distillation from large to small models Across 22 benchmarks, the 2B model outperforms similarly sized SOTA systems on 16 tasks. The 32B model approaches frontier-level performance. 🔗 GitHub: 🤗 Hugging Face:
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We're releasing HY-Embodied-0.5, a family of foundation models for real-world embodied agents. The 2B model is now open source. It strengthens spatial-temporal perception and embodied reasoning for prediction, interaction, and planning. 🤖 The suite includes: 🔹 2B for edge deployment 🔹 32B for complex reasoning Key innovations: 🔹 Mixture-of-Transformers (MoT) architecture for modality-specific computation 🔹 Latent tokens for improved perceptual representation 🔹 Self-evolving post-training 🔹 On-policy distillation from large to small models Across 22 benchmarks, the 2B model outperforms similarly sized SOTA systems on 16 tasks. The 32B model approaches frontier-level performance. 🔗 GitHub: 🤗 Hugging Face:

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Big Models. Complex Projects. Running Local. Our 128GB agent computers are now regularly loading and running large models like Step 3.7 and Qwen 3.5. 122B for heavy reasoning and overnight work using Ryzen AI Max+ 395 processors.
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Big Models. Complex Projects. Running Local. Our 128GB agent computers are now regularly loading and running large models like Step 3.7 and Qwen 3.5. 122B for heavy reasoning and overnight work using Ryzen AI Max+ 395 processors.

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