Self-supervised Hierarchical Visual Reasoning with World Model
The paper introduces ResDreamer, a hierarchical world model designed for self-supervised visual reasoning in 3D open-world environments. It emphasizes the importance of informative, task-relevant signals over photorealistic fidelity in visual reasoning. The proposed model achieves state-of-the-art sample and parameter efficiency, enhancing the capabilities of online reinforcement learning agents.
- ▪ResDreamer is a hierarchical world model that reconstructs residuals from lower layers to improve reasoning.
- ▪The model focuses on providing task-relevant signals rather than photorealistic visual fidelity.
- ▪Experiments demonstrate that ResDreamer achieves superior sample and parameter efficiency compared to existing methods.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.17537 |
| Publication time | Tue, 19 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-19T04:04:57.272Z |
| Last seen | 2026-05-19T04:04:57.272Z |
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| Commercial reuse | May the content be reused commercially? | Not permitted |
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Computer Science > Artificial Intelligence arXiv:2605.17537 (cs) [Submitted on 17 May 2026] Title:Self-supervised Hierarchical Visual Reasoning with World Model Authors:Yuanfei Xu, Lin Liu, Wengang Zhou, Mingxiao Feng, Houqiang Li View a PDF of the paper titled Self-supervised Hierarchical Visual Reasoning with World Model, by Yuanfei Xu and 3 other authors View PDF HTML (experimental) Abstract:3D open-world environments with adversarial opponents remain a core challenge for reinforcement learning due to their vast state spaces. Effective reasoning representations are essential in such settings.
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