Kyutai Releases REKI, a 2.4B Vision-Language-Action Model with Cached Register Memory
Kyutai, with Inria and École des Ponts, has released REKI, a 2.4B-parameter vision-language-action model for robot manipulation that was pretrained only on public data. It pairs a Qwen3.5-2B backbone with a 200M-parameter flow-matching action expert, and adds memory through 32 learned register tokens cached over the last three frames rather than feeding past frames into the VLM. The authors report 90.4% on LIBERO-Plus and 65.8% on RoboTwin Clean2Rand, with plain-language controls cutting arm oscillation on RoboTwin from 18.6% to 4.1%. Weights are on Hugging Face under CC BY-NC-SA 4.0, with six checkpoints including benchmark finetunes.
Why it matters: It offers a concrete, openly released VLA and a cheap memory design that robotics practitioners can inspect, reproduce and finetune.