Compositional Average Velocity Modeling for Efficient Diffusion Offline Reinforcement Learning
Diffusion-based policies have recently emerged as a powerful paradigm for offline reinforcement learning, but their reliance on multi-step denoising introduces substantial computational overhead and requires backpropagation through time during training, limiting scalability and practical deployment. In this work, we propose \textbf{Instant Flow Q-Learning (InsQL)}, a single-step generative policy learning framework that retains the expressive capacity of diffusion models while eliminating iterative sampling and BPTT. InsQL builds on the flow-matching perspective of diffusion policies, but replaces marginal velocity parameterization with \emph{average-velocity} modeling and introduces a pr...
| Authors | Thanh Nguyen, Abu Hanif Muhammad Syarubany, Hwanhee Kim et al. |
|---|---|
| Primary category | cs.LG |
| Published | 10 October 2026 |
| Last updated at source | 10 October 2026 |
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