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What Chelsea Finn thinks about reinforcement learning

@chelsea-finn · 22 positions · 0 changes of mind

Assistant professor of computer science at Stanford, and co-founder of the robot foundation-model company Physical Intelligence.

Chelsea Finn did not write this page.

We collected these quotes from things they published elsewhere, and every quote links to where it was said. They have no account here and have not endorsed this site. Quotes are word for word; the short line under each one is our own restatement, not their wording. Their own site. Is this you? Claim it or ask us to remove it. Or tell us what is wrong here.

6 dated positions, 2026, in their own words. Our reading of what Chelsea Finn has said — not written or endorsed by them.

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  1. Our readingPhysical AI has to make far fewer mistakes than any AI deployed so far, because it is only useful when it acts autonomously on the world.

    Their wordsUh and this means that they're going to be far more useful when they're operating fully autonomously. And as a result, this requires us to develop physical AI systems that make far fewer mistakes than the machine learning systems that have been deployed thus far.

    ↗Chelsea Finn: This is the State of the Art in Roboticsyoutube.com 2nd of 22 in this recording

    Interesting

  3. Our readingReinforcement learning cannot be scaled for robots the way it was for language models, because every attempt spends real robot hours instead of data-centre compute.

    Their wordsNow maybe this isn't completely out of the question but this would be quite challenging uh to do and that's because the calculus is a little bit different. We're not just running compute to optimize for a use case. We're actually running the robot in the real world and using the hardware and attempting the task in the real world.

    ↗Chelsea Finn: This is the State of the Art in Roboticsyoutube.com 4th of 22 in this recording

    robotics

    Interesting

  5. Our readingWhether a robot policy is any good is settled by running it for thirteen hours straight, not by whether it completes the task once.

    Their wordskind of going back to this reliability question, we took this policy and we ran it not just once, but we ran it for 13 hours straight. Uh and we basically wanted to evaluate is this policy not only good at making a latte once, but can it do so reliably to the extent that it would be needed to be useful in the real world?

    ↗Chelsea Finn: This is the State of the Art in Roboticsyoutube.com 6th of 22 in this recording

    robotics

    Interesting

  7. Our readingOne pre-trained robot model now matches or beats the specialists that were fine-tuned with reinforcement learning for the very tasks they were built for.

    Their wordswe see that the across the board the single PIO like pre-trained PIO7 model matches or outperforms the fine-tuned specialists that were developed with reinforcement learning post-training for those downstream tasks.

    ↗Chelsea Finn: This is the State of the Art in Roboticsyoutube.com 11th of 22 in this recording

    scaling lawsrobotics

    Interesting

  9. Our readingRobot models will reach ChatGPT-level capability within the next few years, even without a ChatGPT-style moment.

    Their wordsat the same time in terms of the capabilities of these models I think that we are really starting to get to the point where these models are actually useful in the real world and I think that getting to the kind of the capabilities of chat GBT I think is um yeah very much on the horizon in the next few years.

    ↗Chelsea Finn: This is the State of the Art in Roboticsyoutube.com 15th of 22 in this recording

    OpenAIrobotics

    Interesting

  11. Our readingMost future robotics training data will be robots' own autonomous attempts, the way most language-model data is now generated by the model itself.

    Their wordsUh and just like how we see in language models how now a lot of time is spent actually generating data, generating synthetic data by actually running the model and having it think through things. I think a lot of the data in the future in robotics is going to be the robot attempting to do lots of tasks in lots of real world circumstances.

    ↗Chelsea Finn: This is the State of the Art in Roboticsyoutube.com 18th of 22 in this recording

    robotics

    Interesting