Researchers have introduced Dwell, a novel model-based reinforcement learning framework that utilizes pretrained vision-language models (VLMs) as recurrent latent transition operators. This approach enables agents to learn and adapt within their own latent worlds, leveraging the VLM's internal representations for policy improvement. Dwell achieves strong performance across various interactive visual environments while significantly reducing computational costs associated with imagination, demonstrating that effective world models for VLM agents need to predict useful actions rather than reconstruct the future. AI
IMPACT This framework could enable more efficient and adaptable AI agents by leveraging VLMs for continuous learning and prediction in latent spaces.
RANK_REASON The item is an academic paper detailing a new framework and methodology for AI agents. [lever_c_demoted from research: ic=1 ai=1.0]
- alphaXiv
- arXiv
- CatalyzeX
- Connected Papers
- CORE Recommender
- DagsHub
- Dwell
- Gotit.pub
- Hugging Face
- Influence Flower
- Litmaps
- Reinforcement Learning
- ScienceCast
- scite Smart Citations
- Vision-Language Models
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