REKI gives a 2.4B robot model cheap memory for longer tasks
Kyutai’s 2.4B REKI lets researchers and robot builders run a vision-language-action model trained only on public data, with performance close to roughly twice-sized policies on three benchmarks. It caches 32 small register tokens from recent camera frames inside the action expert, so each frame is encoded once instead of adding a separate memory module. That cut pose-oscillation failures by about fourfold, and Kyutai released the data, training, inference, evaluation, deployment code, and model weights.