Latent-dynamics transitions for GFlowNets

Determine whether Langevin-dynamics-based latent transition mechanisms can be extended to GFlowNets and identify when such mechanisms accelerate training.

Background

The paper compares persistent Gibbs updates with successful Markov samplers that simulate latent dynamics at each transition, including Langevin-dynamics-based methods. It leaves unresolved whether an analogous latent-dynamics construction is applicable to GFlowNets and under what conditions it would improve training efficiency.

References

Successful Markov samplers, such as % HMC \citep{neal2011mcmc} and Langevin dynamics-based methods \citep{Welling2011,girolami2011riemann}, rely on simulating a latent dynamics for each transition of the underlying stochastic process; is such a technique extensible to GFlowNets, and when does it accelerate training? % improve state space exploration? % and accelerate convergence?

— Particle GFlowNets: Rethinking Generative Marginalization Models  (2609.11538 - Silva et al., 10 Sep 2026) in Section 6, Discussion