DADiff · Jul 17, 2026
DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning
A paper titled 'DADiff: Diffusion-Driven Cross-Domain Policy Adaptation for Reinforcement Learning' was published on arXiv on 2026-07-17. It introduces a diffusion-based framework for online dynamics adaptation in reinforcement learning, using generative trajectory discrepancy to estimate dynamics mismatch between source and target domains. The framework includes reward modification and data selection variants, and provides a theoretical bound on policy performance difference based on generative trajectory deviation.
What happened
The paper proposes DADiff, a diffusion-based framework for cross-domain policy adaptation in reinforcement learning. It addresses the challenge of dynamics mismatch by leveraging the discrepancy between source and target domain generative trajectories during next-state generation. Two variants—reward modification and data selection—are developed for policy adaptation. A theoretical analysis bounds the performance difference of a policy between domains by the generative trajectory deviation. The work is presented as a generative modeling approach to domain adaptation, contrasting with prior methods using domain classifiers or representation learning.
Technical significance
DADiff uses a diffusion model to generate next states and quantifies dynamics mismatch through the discrepancy between source and target domain generative trajectories. This discrepancy is then used to adapt the policy via reward modification or data selection. The theoretical contribution shows that the performance gap of a policy across domains is bounded by this generative trajectory deviation, providing a principled basis for the adaptation methods.
Industry impact
This research could improve the robustness and transferability of reinforcement learning policies in real-world applications where dynamics vary, such as robotics or autonomous systems. By enabling adaptation with limited target-domain interactions, it reduces the need for extensive retraining, potentially lowering deployment costs and time.
What to watch
Future work may explore the applicability of DADiff variants to different domains and extend the theoretical connections. Empirical validation on diverse benchmarks and real-world tasks will be key next signals. Integration with other generative models or combination with representation learning could further enhance adaptation efficiency.
Decision value
The framework can reduce the cost and time required to adapt RL policies to new environments, making it valuable for industries deploying AI in dynamic settings, such as manufacturing, logistics, and autonomous vehicles. It enables faster scaling of RL solutions across varied operational conditions.