Kyutai Releases REKI: A 2.4B Vision-Language-Action Model for Robot Manipulation with Efficient Memory

Kyutai has introduced REKI, a 2.4 billion parameter Vision-Language-Action (VLA) model purpose-built for robot manipulation tasks, incorporating an efficient memory architecture that enables it to retain and leverage task-relevant context across manipulation sequences. VLA models that integrate vision, language understanding, and action prediction into a single model are a key research direction for generalizable robotics, and REKI's relatively compact 2.4B size makes it more accessible for real-hardware deployment compared to larger VLA systems. The efficient memory mechanism addresses a practical pain point in robotic manipulation — maintaining state across multi-step tasks without prohibitive compute overhead. Developers working on robotics stacks or physical AI systems will want to evaluate REKI as a candidate backbone for manipulation pipelines. Kyutai's release adds a new competitive open option in the VLA space alongside models from Google DeepMind and other frontier robotics researchers.