Recovering Hidden Reward in Diffusion-Based Policies

Researchers posted a new paper, “Recovering Hidden Reward in Diffusion-Based Policies,” on arXiv and Hugging Face.

The work focuses on inferring latent reward signals inside diffusion-based policy models.

This could help policy-learning systems recover objectives even when rewards are not directly observed.

The idea is especially relevant for robotics and other reinforcement learning applications.