From the abstract, emphasis mine:

The agent, which we refer to as Gato, works as a multi-modal, multi-task, multi-embodiment generalist policy. The same network with the same weights can play Atari, caption images, chat, stackblocks with a real robot arm and much more, deciding based on its context whether to output text, joint torques, button presses, or other tokens.

(Will edit to add more as I read. ETA: 1a3orn posted first.)

  1. It's only 1.2 billion parameters. (!!!) They say this was to avoid latency in the robot control task.
  2. It was trained offline, purely supervised, but could in principle be trained online, with RL, etc
  3. Performance results:

The section on broader implications is interesting. Selected quote:

In addition, generalist agents can take actions in the the physical world; posing new challenges that may require novel mitigation strategies. For example, physical embodiment could lead to users anthropomorphizing the agent, leading to misplaced trust in the case of a malfunctioning system, or be exploitable by bad actors. Additionally, while cross-domain knowledge transfer is often a goal in ML research, it could create unexpected and undesired outcomes if certain behaviors (e.g. arcade game fighting) are transferred to the wrong context. The ethics and safety considerations of knowledge transfer may require substantial new research as generalist systems advance. Technical AGI safety (Bostrom, 2017) may also become more challenging when considering generalist agents that operate in many embodiments. For this reason, preference learning, uncertainty modeling and value alignment (Russell, 2019) are especially important for the design of human-compatible generalist agents. It may be possible to extend some of the value alignment approaches for language (Kenton et al., 2021; Ouyang et al., 2022) to generalist agents. However, even as technical solutions are developed for value alignment, generalist systems could still have negative societal impacts even with the intervention of well-intentioned designers, due to unforeseen circumstances or limited oversight (Amodei et al., 2016). This limitation underscores the need for a careful design and a deployment process that incorporates multiple disciplines and viewpoints. 

They also do some scaling analysis and yup, you can make it smarter by making it bigger.

What do I think about all this?

Eh, I guess it was already priced in. I think me + most people in the AI safety community would have predicted this. I'm a bit surprised that it works as well as it does for only 1.2B parameters though. 

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The two major points I take away:

  1. Scaling Just Works: as blase as we may now be at seeing 'lines go straight', I continue to be shocked in my gut that they do just keep going straight and something like Gato can be as straightforward as 'just train a 1.2b-param Transformer on half a thousand different tasks, homes, nbd' and it works exactly like you'd think and the scaling curve looks exactly like you'd expect. It is shocking how unshocking the results are conditional on a shocking thesis (the scaling hypothesis). So many S-curves and paradigms hit an exponential wall and explode, but DL/DRL still have not. We should keep this in mind that every time we have an opportunity to observe scaling explode in a giant fireball, and we don't.

  2. Multi-task learning is indeed just another blessing of scale: as they note, it used to be that learning multiple Atari games in parallel was really hard. It did not work, at all. You got negative transfer even within ALE. People thought very hard and ran lots of experiments to try to create things like Popart less than 4 years ago where it was a triumph that, due to careful engineering a single checkpoint could play just the ALE-57 games with mediocre performance.

    Decision Transformer definitely made 'multi-task learning is a blessing of scale' the default hypothesis, but no one had actually shown this, the past DT and other work (aside from MetaMimic) were all rather low n and k; you could wonder if they would interfere at a certain point or break down, and require fancy engineering like MoEs to enable learning at all. (Similarly, Andy Jones showed nice scaling laws for DRL and I scraped together a few examples like Ms Pacman, but nothing across really complicated tasks or many tasks.)

    Now you can throw in not just ALE, but DMLab, Metaworld, Procgen, hell, let's just throw in a bunch of random Internet scraped text and images and captions and treat those as 'reinforcement learning tasks' too why not, and to make them all play together you do... nothing, really, you just train on them all simultaneously with a big model in about the dumbest way possible and it works fine.

(Also, if one had any doubts, DM is now fully scale-pilled.)

If anyone was wondering whether DM planned to follow it up in the obvious way because of the obvious implications of its obvious generality and obvious scalability, Hassabis says on the Fridman podcast: " it's just the beginning really, it's our most general agent one could call it so far but um you know that itself can be scaled up massively more than we've done so far obviously we're in the in the middle of doing that."