AI RESEARCH
Value Gradient Sampler: Learning Invariant Value Functions for Equivariant Diffusion Sampling
arXiv CS.LG
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ArXi:2502.13280v3 Announce Type: replace We propose the Value Gradient Sampler (VGS), a diffusion sampler parameterized by value functions. VGS generates samples from an unnormalized target density (i.e., energy) by evolving randomly initialized particles along the gradient of the value function. In many sampling problems where the target density exhibits invariant symmetries, value functions provide a novel approach to leveraging invariant networks for sampling by inducing an equivariant gradient flow, without requiring complex equivariant networks.