4 accepted papers
Flow-based generative models, including diffusion models, excel at modeling continuous distributions in high-dimensional spaces. In this work, we introduce Flow Policy Optimization (FPO), a simple on-policy reinforcement learning algorithm that brings flow matching into the policy gradient framework…
Large-scale AI model training divides work across thousands of GPUs then synchronizes gradients across them at each step. This incurs a significant network burden that only centralized, monolithic clusters can support, driving up infrastructure costs and straining power systems. We propose Decentral…
How can we teach humanoids to climb staircases and sit on chairs using the surrounding environment context? Arguably the simplest way is to _just show them_—casually capture a human motion video and feed it to humanoids. We introduce **VideoMimic**, a real-to-sim-to-real pipeline that mines everyday…
Score distillation sampling (SDS) has proven to be an important tool, enabling the use of large-scale diffusion priors for tasks operating in data-poor domains. Unfortunately, SDS has a number of characteristic artifacts that limit its utility in general-purpose applications. In this paper, we make…