arXiv stat.ML
· Papers
Expressivity and Statistical Trade-offs in Diffusion Policy Learning
arXiv:2607.07967v1 Announce Type: new Abstract: Diffusion-based policies have recently emerged as powerful policy parameterizations for reinforcement learning, representing state-conditioned action distributions as terminal laws of diffusion processes with parameterized drifts. This terminal-law representation has show