CFG-Policy: Unified Diffusion Policy Learning Framework via Classifier-free Guidance
Diffusion policies are increasingly used in robotic control and manipulation, vision-language-action models, and world-model-based agents. Their ability to represent multimodal action distributions offers rich opportunities for exploration in reinforcement learning (RL). However, optimizing diffusion policies with RL can reduce the behavioral diversity and further limit their exploration capability. In RL post-training, directly fine-tuning a pretrained policy may also disrupt its acquired capabilities, while action-space residuals may lack the expressive power for multimodal exploration. Together, these issues reveal a common challenge across online and offline-to-online diffusion RL: im...
| Authors | Shutong Ding, Zhaoyang Liu, Yimiao Zhou et al. |
|---|---|
| Primary category | cs.RO |
| Published | 10 October 2026 |
| Last updated at source | 10 October 2026 |
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