@IdanShenfeld
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Introducing Inkling 2 Small. Scoring 31 on the AA Intelligence Index, it's a big jump over its predecessor.View quoted post on X
Inkling 2 Small is available for fine-tuning and inference on Tinker today, with full weight release planned soon.
thinkingmachines.ai/news/inkling-2…
SFT is not dead! 🥳
We found a way to make SFT rival current prevailing posttraining methods, often generalizing better and forgetting less than RL and OPSD. 🤯
Following our prior work on reasoning with sampling, we now introduce sampling to the posttraining stack.
1/nView quoted post on X
(1/7) Humans can acquire many capabilities without (catastrophically) forgetting previous ones. Why can't neural networks do the same?
Local Support Learning (LSL) takes us a step closer towards this goal.
In LLMs of up to 7B parameters, LSL augments gradient-based training to retain prior capabilities while learning new tasks at full capacity. Notably, it does so without access to prior data.
Key idea: casting forgetting as a geometric problem, and optimizing for the worst case.
Paper: arxiv.org/abs/2610.02126
Website and code: assafbk.github.io/lslView quoted post on X
X-rays are everywhere in medicine, but extracting general, quantitative anatomical information from them remains remarkably difficult.
Announcing FleXray: an open-source, open-weight model for zero-shot anatomical segmentation across the human body—from different regions, viewing angles, and acquisition settings.
FleXray learns from large-scale synthetic supervision generated from 3D anatomy, then transfers directly to real clinical X-rays.
Try it now on your own X-rays in our in-browser demo!
🧵 1/N — project page, demo, weights, code & paper below ↓View quoted post on X
