@svlevine
Associate Professor at UC Berkeley Co-founder, Physical Intelligence
@barak1724 I believe there was a Slideslive recording! I assume this means it shows up on the ICML website eventually, I believe they initially make it available only to registered attendees and then eventually release it.
Starting now in ASEM Ballroom 203 after a bit of AV troubles :) see you there! The talk will cover how to build up a general framework for data-driven decision making! x.com/svlevine/statu…
This morning at ICML 2026, I'll be speaking at the Workshop on Decision-Making from Offline Datasets. Come to ASEM Ballroom 203 at 8:15 am KST! I'll talk about foundations of data-driven decision making, from design via model-based optimization to offline RL
If you want a robot to do something well, you need to know how to talk to it. If you don't, you can learn, with Semantic Action RL! In our paper, @JagdeepBhatia8, @ajwagenmaker, @verityw_ show how RL over VLA prompts enables new tasks and learns blazing fast in the real world!
The idea is simple: instead of running RL over robot actions, run RL over language commands to a VLA. This is much easier, because any vision-language model worth its salt provides a great prior over likely semantic commands, so RL has a much smaller search space.
We can learn a model that provides shaped "process rewards" for robotic RL, that evolves automatically as the policy gets better. This improves performance on benchmarks, and works in the real world! Some fun new work with Raymond Tsao & @ajwagenmaker
In the real world, this leads to drastically faster learning, in just a few thousand time steps.
A new way to do off-policy RL with diffusion: if we have off-policy data, we need to figure out what the diffusion latent steps for it would be with our *current* policy (not the one that collected it), so this requires reversing the diffusion process on off-policy data. x.com/aditya_oberai/…
Flow reversal steering allows "steering" diffusion-based VLAs with high-level actions, for example from VLM reasoning. This also lets us run RL in the diffusion noise space with exploration guided by high-level reasoning: think through a task, then practice it! 👇
Check out the paper here: flow-reversal-steering.github.io A fun collaboration w/ Andy Tang, @verityw_ , @ajwagenmaker, @chelseabfinn
Diffusion (or flow) makes for excellent policies, but training them with RL is notoriously hard: BPTT is unstable, RL over diffusion blows up the horizon. In our new paper, we show how we can optimize flow matching actors by using "one weird trick" -- "approximate" the Jacobian of the flow denoising process with the identity matrix. 👇
Our method (QGF) outperforms using the true Jacobian or BPTT. It is entirely a test-time method (i.e., the policy is trained with BC, the Q-function is trained with TD, and at test-time, optimize the Q-function wrt actions using the identity Jacobian "approximation").
