580 citations · 1.9k across the 92 of their papers we have counts for
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DiPOD: Diffusion Policy Optimization without Drifting Apart
Haozhe Jiang, Haiwen Feng, Pieter Abbeel +3
RL post-training has become increasingly pivotal for improving diffusion policies, but existing diffusion policy-gradient methods are often unstable and cannot achieve reliable pol…
Remember to be Curious: Episodic Context and Persistent Worlds for 3D Exploration
Lily Goli, Justin Kerr, Daniele Reda +3
Exploration is a prerequisite for learning useful behaviors in sparse-reward, long-horizon tasks, particularly within 3D environments. Curiosity-driven reinforcement learning addre…
Flow Matching Policy Gradients
David McAllister, Songwei Ge, Brent Yi +5
Flow-based generative models, including diffusion models, excel at modeling continuous distributions in high-dimensional spaces. In this work, we introduce Flow Policy Optimization…
Variational Discriminator Bottleneck: Improving Imitation Learning, Inverse RL, and GANs by Constraining Information Flow
Xue Bin Peng, Angjoo Kanazawa, Sam Toyer +2
Adversarial learning methods have been proposed for a wide range of applications, but the training of adversarial models can be notoriously unstable. Effectively balancing the perf…