Kyutai keeps proving that small open models are its lane — after Moshi, REKI extends that playbook into robotics. The genuinely interesting bet is the cheap temporal memory: most VLA policies are effectively reactive, and memory of the recent past could matter more for manipulation than raw scale. Open weights plus public-data-only pretraining make this an instant baseline for academic labs priced out of proprietary pipelines. Our caveat: manipulation benchmarks, especially simulated OOD ones, tend to flatter small models, so real-world transfer is the real test. Still, a 2.4B model trailing only 2x-larger systems is a solid result — and a hint that robot learning may not follow the brute-scale path of LLMs.