Semore Enhances Visual Reinforcement Learning Representations
Researchers led by Wentao Wang (arXiv v1, Dec. 4, 2025) introduce Semore, a VLM-based framework for visual reinforcement learning that jointly extracts semantic and motion representations via a dual-path backbone operating on RGB flows. The method uses vision-language models and CLIP text–image alignment to embed commonsense-grounded features and applies separate supervision to fuse semantics and motion. Experiments report improved efficiency and adaptivity versus prior state-of-the-art, and code is released.
Key Points
- 1Introduces dual-path backbone extracting semantic and motion representations from RGB optical-flow inputs
- 2Leverages vision-language models and CLIP alignment to embed commonsense-grounded features into the backbone
- 3Enables more efficient adaptive decision-making in visual RL with separately supervised semantic-motion fusion
Scoring Rationale
VLM-guided dual-path fusion and released code drive relevance, but contribution is incremental and limited to visual-RL preprint.
Sources
Public references used for this report.
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