We address the problem of fine-tuning pre-trained generative policies with reinforcement learning while preserving the multimodality of the action distributions of such policies. Current methods for fine-tuning generative policies (e.g. diffusion policies) with reinforcement learning improve task performance but tend to collapse diverse behaviors into a single reward-maximizing mode. To overcome this, we propose MD-MAD, an unsupervised mode discovery framework that uncovers latent behaviors in generative policies, together with a mutual information metric to quantify multimodality. The discovered modes allow mutual information to be used as an intrinsic reward, regularizing reinforcement learning fine-tuning to improve success rates while maintaining diverse strategies. Experiments on robotic manipulation tasks demonstrate that our method consistently outperforms conventional fine-tuning, achieving high task success while preserving richer multimodal action distributions.
Unsupervised mode discovery for fine-tuning multimodal action distributions
| Alberta Longhini, David Emukpere, Jean-Michel Renders, Seungsu Kim |
| The Forty-Third International Conference on Machine Learning (ICML), Seoul, South Korea, 6-11 July, 2026 |
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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