#actor-critic

'A compact neural agent learns to plan, store and compose symbolic tools end-to-end with reinforcement learning, demonstrating emergent multi-step reasoning on synthetic arithmetic tasks.'

DSRL introduces a novel method to adapt diffusion-based robotic policies via latent-space reinforcement learning, significantly boosting real-world task performance without modifying base models.