We shared more details on the Meta PARTNR work to unlock human-robot collaboration in this post https://
go.fb.me/073dkm
@aiatmeta
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Meta PARTNR: Advancing Human-Robot Collaboration Details
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AI Model Deployment Challenges for Real-Time Robotics
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One of the very interesting challenges with AI models for robotics is the constraints you're working with when deploying models that need to run in realtime on-device.
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CollaborativeAgentBench: First Multi-Turn Human-Agent Collaboration Benchmark
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New agents benchmark: CollaborativeAgentBench is the first benchmark studying collaborative LLM agents that work with humans across multi-turn collaboration on realistic tasks in backend programming & frontend design
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SWEET-RL: Novel Algorithm for Long-Horizon Multi-Turn Tasks
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As part of this work, we’re also releasing SWEET-RL, a novel RL algorithm for long-horizon & multi-turn tasks which can perform better credit assignments. Our experiments demonstrate that SWEET-RL achieves a 6% absolute improvement in success & win rates on
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Llama Surpasses 1 Billion Downloads Milestone
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Llama has now been downloaded over 1 Billion times! A note to:
The researchers at Meta training these models — and those building on the research in other labs.
The developers and enthusiasts on r/LocalLlama, @huggingface and more; experimenting with new models and creating -
Meta’s UKDSL Research Improves Ad Ranking with Unlabeled Data
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New ads ranking research from Meta researchers. Compared to prior work, UKDSL can enable models to learn from a much larger set of unlabeled data, improving performance while being computationally efficient https://
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Llama Models Power India’s First Open Source Audio Language Model
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Llama models were used to develop India's first open source audio language model — Shuka v1 https://
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Orakl Oncology’s DINOv2 Model Implementation from Meta FAIR
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We shared more on Orakl Oncology's work with the open source DINOv2 model from Meta FAIR in this post
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AI Model Accelerates Cancer Therapy Research Through Organoid Analysis
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AI is helping researchers identify therapies for cancer patients. @orakldotbio trained our DINOv2 model on organoid images to more accurately predict patient responses in clinical settings. This approach outperformed specialized models and is helping accelerate their research. pic.twitter.com/7ptP6RcyL3
— AI at Meta (@AIatMeta) 11 mars 2025AI is helping researchers identify therapies for cancer patients. @orakldotbio trained our DINOv2 model on organoid images to more accurately predict patient responses in clinical settings. This approach outperformed specialized models and is helping accelerate their research.
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uCO3D: 170K Videos with 3D Annotations for Object Recognition
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Highlights of uCO3D:
• 170K videos depicting diverse objects from all directions.
• Objects spanning ~1000 categories — grouped into 50 super-categories.
• Full original videos annotated with object segmentation, camera poses & point clouds.
• 3D Gaussian Splat