Andy Chen

Praxis-1, our first world action model, is now available for anyone to use. It is currently the leading open-weights robotics model on the RoboLab leaderboard and outperforms every other world action model on the benchmark. More than that, it’s versatile and easy to fine-tune for new hardware and tasks. We’ve tested it on 9 different robot embodiments so far, in simulation and on real hardware with multiple partners, and we’ve seen it reach good performance on a new embodiment with under 30 minutes of demonstrations. Weights, code and fine-tuning recipes are on GitHub and Hugging Face. It can run on a single RTX 5090 with NVFP4. We started our robotics work about a year ago with the thesis that the same video pretraining we do for our creative models can be massively useful for building systems that interact with the physical world, and the benefits of transfer are now becoming clear. When I first talked to people in the field, there was a lot of skepticism about whether video models would be useful in robotics at all, and then about whether you could use them as policies. From first principles, it makes sense that if you can predict the future well in pixels, extracting an action representation should be the easy part. Reality caught up with first principles. One of my favorite things in our technical report is the idea of self-rollouts. Because the model’s performance is bottlenecked by its visual imagination, you can quickly get an intuition for whether it will perform a new task well by rolling out the policy in the model’s imagination. That can speed up evaluation, and it opens a path to doing reinforcement learning in the model’s imagination. There’s been a lot of discussion recently about frontier LLMs being the bitter-pilled way to solve robotics. But scaling performance from unsupervised video is an even better example of the Bitter Lesson than scaling from text. Praxis-1 is more than 10x faster and 50x cheaper than GPT-6 Astra on the manipulation tasks we tested. Frontier LLMs will keep improving rapidly on the Pareto frontier of cost and performance, but so will world models. There are also great ways of combining the strengths of the two. And we are much farther from saturating multimodal data than we are text. Thanks to the mighty Runway Robotics team for making this happen, and to our partners for being so generous with their time helping us test the model across a wide range of hardware and tasks.

Runway turned eight years old this week. We started at a time when generative models were a research curiosity, and had to figure out how to run an AI company from first principles. Growing this organization has been one of the biggest joys of my life. Below is a short note that I sent the team: Thank you for the kind words, everyone. The reality is, as convenient as it might be to portray otherwise, the Runway of today looks nothing like the Runway of 2018. And that's by design. We didn't know we'd be focusing on video, let alone pretraining video models at scale, let alone pursuing world models. It's a convention to rewrite the origin story to fit the present. But this company is all about breaking conventions, so let's do away with that one too, shall we? So then, what is Runway about? I think Runway is a story about betting on people. By far the most gratifying moments of my time here have been watching people get an opportunity and then prove themselves far beyond what anyone expected. The person with zero publications who became one of the most important researchers in the community. The person who barely spoke in meetings and became one of the best managers in the company. You know who you are. And it's the three of us too: Alejandro, Cris, and me. Three people from an art school with zero connections should not have built a company that made it this far. It wasn't supposed to happen. It did because we got lucky with the people we brought on. I desperately want the story and legacy of this company to be about how good people win. In the world we live in, the default is that the loudest people win, no matter how wrong they are. We've beaten many loud-but-wrong competitors, but we can't take that for granted. The odds favor the loud over the right, because loudness is noticed immediately and being right takes time to be noticed. So when you know you are right, please be louder than you think you should be, even when it feels uncomfortable. The world will be better for it. And let's all make the ninth year of Runway the loudest one yet.