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Building technologies for better human-AI collaboration on next gen hardware at scale. Exciting. Thinking Machines (@thinkymachines) We are partnering with @nvidia to power our frontier model training and platforms delivering customizable AI. thinkingmachines.ai/news/nvidia-partnership/ [Translated from EN to English]
→ View original post on X — @lilianweng, 2026-03-10 17:08 UTC
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On-policy distillation provides an elegant way to use the teacher model as a process reward model to provide dense reward while preventing SFT style "OOD shock" during rollout. Thinking Machines (@thinkymachines) Our latest post explores on-policy distillation, a training approach that unites the error-correcting relevance of RL with the reward density of SFT. When training it for math reasoning and as an internal chat assistant, we find that on-policy distillation can outperform other approaches for a fraction of the cost. thinkingmachines.ai/blog/on-… — https://nitter.net/thinkymachines/status/1982856272023302322#m
→ View original post on X — @lilianweng, 2025-10-27 17:31 UTC
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GPUs are expensive and setting up the infrastructure to make GPUs work for you properly is complex, making experimentation on cutting-edge models challenging for researchers and ML practitioners. Providing high quality research tooling is one of the most effective ways to improve research productivity of the wider community and Tinker API is one step towards our mission there. Tinker API is built on top of our experimental results on fine-tuning with LoRA: thinkingmachines.ai/blog/lor… Beta starts and you can join the waitlist today: thinkingmachines.ai/tinker/
→ View original post on X — @lilianweng, 2025-10-01 18:29 UTC
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Looking through those little hidden gem stories in the footnote, you will find it so inspiring that researchers with interests on the same topic are able to work together to advance a field despite their roles and locations. This is the power of open science and community. Thinking Machines (@thinkymachines) Efficient training of neural networks is difficult. Our second Connectionism post introduces Modular Manifolds, a theoretical step toward more stable and performant training by co-designing neural net optimizers with manifold constraints on weight matrices. thinkingmachines.ai/blog/mod… We explore a fundamental understanding of the geometry of neural network optimization. — https://nitter.net/thinkymachines/status/1971623409873244462#m
→ View original post on X — @lilianweng, 2025-09-26 19:03 UTC
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To be clear, here the (first) Thinking Machines Corporation (en.m.wikipedia.org/wiki/Thin…) I referred to was founded in 1983 🙂
→ View original post on X — @lilianweng, 2025-09-11 02:30 UTC
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Besides the fun fact that Connectionism is connected with the early days of the AI field and highlights similarities between neural networks and human brains, the flagship product of the (first) Thinking Machines is named Connection Machine. — 🧑🎓Enjoy reading and more is coming! Thinking Machines (@thinkymachines) Today Thinking Machines Lab is launching our research blog, Connectionism. Our first blog post is “Defeating Nondeterminism in LLM Inference” We believe that science is better when shared. Connectionism will cover topics as varied as our research is: from kernel numerics to prompt engineering. Here we share what we are working on and connect with the research community frequently and openly. The name Connectionism is a throwback to an earlier era of AI; it was the name of the subfield in the 1980s that studied neural networks and their similarity to biological brains. thinkingmachines.ai/blog/def… — https://nitter.net/thinkymachines/status/1965826369721623001#m
→ View original post on X — @lilianweng, 2025-09-10 17:24 UTC
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Welcome @_kevinlu to the team! Exciting to work together again. Kevin Lu (@_kevinlu) I recently joined @thinkymachines — super excited to work with the team, I think we have the highest density of research talent in the world 🙂 we have a very ambitious roadmap ahead, the right team to work on it, & I think now is a great time to join; you should reach out to the team if that excites you! — https://nitter.net/_kevinlu/status/1957479551602397644#m
→ View original post on X — @lilianweng, 2025-08-18 19:49 UTC
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Yes – 🥳 Thinky starts hiring again: thinkingmachines.paperform.c… Mira Murati (@miramurati) Thinking Machines Lab exists to empower humanity through advancing collaborative general intelligence. We're building multimodal AI that works with how you naturally interact with the world – through conversation, through sight, through the messy way we collaborate. We're excited that in the next couple months we’ll be able to share our first product, which will include a significant open source component and be useful for researchers and startups developing custom models. Soon, we’ll also share our best science to help the research community better understand frontier AI systems. To accelerate our progress, we’re happy to confirm that we’ve raised $2B led by a16z with participation from NVIDIA, Accel, ServiceNow, CISCO, AMD, Jane Street and more who share our mission. We’re always looking for extraordinary talent that learns by doing, turning research into useful things. We believe AI should serve as an extension of individual agency and, in the spirit of freedom, be distributed as widely and equitably as possible. We hope this vision resonates with those who share our commitment to advancing the field. If so, join us. thinkingmachines.paperform.c… — https://nitter.net/miramurati/status/1945166365834535247#m
→ View original post on X — @lilianweng, 2025-07-15 18:11 UTC
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We have been working hard for the past 6 months on what I believe is the most ambitious multimodal AI program in the world. It is fantastic to see how pieces of a system that previously seemed intractable just fall into place. Feeling so lucky to create the future with this talented and aligned team. Mira Murati (@miramurati) Thinking Machines Lab exists to empower humanity through advancing collaborative general intelligence. We're building multimodal AI that works with how you naturally interact with the world – through conversation, through sight, through the messy way we collaborate. We're excited that in the next couple months we’ll be able to share our first product, which will include a significant open source component and be useful for researchers and startups developing custom models. Soon, we’ll also share our best science to help the research community better understand frontier AI systems. To accelerate our progress, we’re happy to confirm that we’ve raised $2B led by a16z with participation from NVIDIA, Accel, ServiceNow, CISCO, AMD, Jane Street and more who share our mission. We’re always looking for extraordinary talent that learns by doing, turning research into useful things. We believe AI should serve as an extension of individual agency and, in the spirit of freedom, be distributed as widely and equitably as possible. We hope this vision resonates with those who share our commitment to advancing the field. If so, join us. thinkingmachines.paperform.c… — https://nitter.net/miramurati/status/1945166365834535247#m
→ View original post on X — @lilianweng, 2025-07-15 17:12 UTC
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I still find it mysterious whether and how intelligence and capabilities transfer between domains and skills – from meta learning during early days to more recent question like whether solving maths helps writing a good essay. Sometime I feel a bit pessimistic given not enough evidence I’ve seen. Would like to get more suggestions and pointers to papers on this topic of generalization in the thread! 🧵
→ View original post on X — @lilianweng, 2025-07-13 21:19 UTC