arXiv:2502.19544v3 Announce Type: replace Abstract: Leveraging offline data is a promising way to improve the sample efficiency of online reinforcement learning (RL). This paper expands the pool of usable data for offline-to-online RL by leveraging abundant non-curated data that is reward-free, of mixed quality, and collected across multiple embodiments. Although learning a world model appears promising for utilizing such data, we find that naive fine-tuning fails to accelerate RL training on many tasks. Through careful investigation, we attribute this failure to the distributional shift betwe
This development appears now as AI research continues to push efficiency boundaries in reinforcement learning, recognizing the practical limitations of curated data in real-world applications.
Improving the efficiency of reinforcement learning with non-curated, multi-source data is crucial for practical, scalable AI deployment, especially in robotics and complex control systems.
The ability to effectively leverage 'dirty', abundant data significantly expands the training data landscape for AI, moving beyond the bottleneck of perfectly curated datasets.
- · AI/ML researchers
- · Robotics developers
- · Companies with abundant unlabelled data
- · AI infrastructure providers
- · Companies reliant solely on curated data
- · Early-stage robotics companies with limited data
- · Inefficient RL methodologies
More robust and generalizable AI models emerge, capable of learning from diverse real-world experiences.
This could accelerate the development and deployment of autonomous systems in complex, unstructured environments, such as humanoid robots.
Reduced data curation costs could democratize advanced AI development, shifting competition towards model architecture and inference efficiency rather than data acquisition advantages.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG