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Excited to share Flow Matching Policy Gradients: expressive RL policies trained from rewards using flow matching. It’s an easy, drop-in replacement for Gaussian PPO on control tasks.
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David McAllister
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PhD Student @berkeley_ai
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Chung Min developed an off-policy flow RL algorithm that’s simple and just works! Check it out!
Excited to introduce QF3: Fast Flow RL with Filtered Q-Gradients!
Train humanoids from scratch, fine-tune VLAs, even fine-tune image models... all with one simple off-policy update ✨View quoted post on X
ABC is fully released, time to build!
Happy to announce the full ABC release! We’re also excited that ABC was accepted to CoRL 2026!
Check out our website for code, 400+ hours of sim data on 24 tasks, and 5,850 labeled policy-evaluation episodes.
@arthurallshire @Cinnabar233 @ritvik_singh9 @redstone_hong @davidrmcallView quoted post on X
I’ll present FDFO at ECCV tomorrow during Poster Session 2 (4:30–6:30 PM CEST), in ExHall near poster board #477! 🐈
We developed a simple, sample-efficient online RL technique for post-training image generation models. We see it as a possible steerable alternative to CFG, driven by any scalar reward, including human preference.View quoted post on X
A huge team effort! Fully open data, infrastructure and eval for the community to build on
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Introducing ABC: open data, training, and infrastructure for robotics.
We release the largest teleop dataset to date, and extensively investigate design decisions, pretraining, and post-training techniques.
@arthurallshire @Cinnabar233 @adamrasb @redstone_hong @davidrmcallView quoted post on X
