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If you have a policy that uses diffusion/flow (e.g. diffusion VLA), you can run RL where the actor chooses the noise, which is then denoised by the policy to produce an action. This method, which we call diffusion steering (DSRL), leads to a remarkably efficient RL method! 🧵👇

Sergey Levine

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153,073 次观看 • 1 年前 •via X (Twitter)

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Sergey Levine 的头像

Sergey Levine1 年前

DSRL trains an actor and Q-function, treating the diffusion noise as the action space. Because samples from the noise prior map to reasonable actions for the policy, DSRL essentially explores "inside" the set of reasonable pre-trained behaviors, making it extremely efficient.

Sergey Levine 的头像

Sergey Levine1 年前

DSRL learns essentially in real time, with good results in as little as 50 trials (it's so efficient that a person can literally sit in front of the robot and push a button to assign sparse rewards).

Sergey Levine 的头像

Sergey Levine1 年前

This was a really fun collaboration led by @ajwagenmaker Project website with paper: To find out more, check out his thread here:

ahad 的头像

ahad1 年前

would a supervised learning version of this work? where the noise distribution is a parmeter that is also optimized along with policy weights

ahad 的头像

ahad1 年前

how long would it take to get that first sparse reward with this method?

Himanshu Kumar 的头像

Himanshu Kumar1 年前

Controlling the noise instead of the action itself is a surprisingly effective approach.

Andres Franco 的头像

Andres Franco1 年前

This is pretty amazing, and the visualization made everything so easy to understand😅

Ran Cheng 的头像

Ran Cheng1 年前

Will making the initial noise distribution a learnable parameter reduce randomness and thus make the model more prone to overfitting?

Joanne Mercado 的头像

Joanne Mercado1 年前

😅🥹

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