Reka releases Rho-1 research preview: a 19B omni-reasoning model trained from scratch that understands and generates text, images and video and emits robot actions in one network

Reka Labs' Rho-1 (blog) is a 19B model trained from scratch that collapses the usual multimodal agent pipeline — a planner LLM handing off to image, video and robotics specialists — into one network with one KV cache. Text and commands are discrete tokens; image/video latents, robot actions and proprioception are continuous tokens. Each transformer block splits into an understanding stream and a generation stream with shared attention; the understanding stream emits a handoff token and the generation stream renders from the full accumulated state via a diffusion head. Training jointly optimizes next-token prediction and flow matching.

Reka publishes no standard benchmark scores, only internal speed numbers: base video at 0.79x real time (median) with a watchable stream in ~6s, and a distilled variant that cuts denoising from 99 to 8 steps and returns a 5.3s clip in about a second. Robotics is shown on LIBERO simulation demos without success-rate tables. Admitted limits: 672x384 native video, long-horizon structural drift, weak temporal grounding and brittle edits. The checkpoint used 320 H100s for three months. Rho-1 is a research preview with no open weights or public API; collaboration is via [email protected]. For developers the takeaway is architectural: a single model with shared state as an alternative to multi-model orchestration for robotics, simulation and interactive video.