SOURCE-LINKED INTELLIGENCE

Physical policy gradient theorem for in situ stochastic-adjoint training

arXiv · AI, language, vision and robotics · article · Sep 5, 2026 · UTC

In situ adjoint training extracts parameter gradients directly from measurement, but has so far been limited to reciprocal or restricted systems. Here, we introduce the physical counterpart of the policy gradient theorem: a stochastic-adjoint gradient estimator that lifts these constraints by trading reciprocity for nondegenerate diffusion. As validation, we train a nonlinear resonator network, whose own dynamics supply the policy, against antagonistic temporal modulations with gradients from measured stochastic trajectories alone, without finite differences or a separate adjoint experiment.

Read original source ↗ Open in workspace

recordType

paper

region

Global