Kyutai releases REKI robot control model with efficient memory
MarkTechPost: Meet Kyutai REKI: The 2.4B VLA Model Bringing Efficient Memory to Robot Manipulation
Kyutai, with Inria and École des Ponts, has released REKI, a 2.4B vision-language-action model (VLA) for robot manipulation. The REKI vision-language-action model is pretrained only on public data. It also carries a cheap memory of its recent past. On three out-of-distribution benchmarks, only models about twice its size beat it. All weights are live on Hugging Face today. TL;DR Size: 2.4B parameters (2.2B Qwen3.5-2B VLM + 0.2B flow-matching action expert). Memory covers the last 3 fram
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REKI is a vision-language-action model with 2.4 billion parameters designed to control robots by interpreting camera views and instructions to generate actions. The model matters because it achieves top performance among models trained only on public data while remaining smaller and more efficient than comparable systems, adding memory capability through cached register tokens that cost only 0.1 milliseconds per action. REKI uses a memory system that stores information from the previous three frames within its action expert component, which reduces problematic oscillations between poses and improves performance on out-of-distribution tasks. The weights are openly available but restricted to non-commercial use.
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