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Reinforcement World Model Learning for LLM-based Agents research paper by Microsoft, 2026

Microsoft · Feb 5, 2026 · Multimodal and robotics · 16 citations · 28 upvotes · unverified

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What it shows

Reinforcement World Model Learning enables LLM-based agents to better anticipate action consequences and adapt to environment dynamics through self-supervised training that aligns simulated and real-world state transitions in embedding space.

UnverifiedHugging Face's summary; not yet checked by hand.

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About this paper
Authors
Xiao Yu, Baolin Peng, Ruize Xu and 6 more
arXiv
2602.05842 · PDF
Venue
arXiv.org
Citations
16, 3 influential · Semantic Scholar
Upvotes
28 · Hugging Face
Lab
Microsoft · on Companies · on Acquisitions · on Quarterly · on Paydays · on Releases · on TechConf

Changes

What changed
Influential citationsfirst count: 3Sep 25, 2026
Citationsfirst count: 16Sep 25, 2026
New paperFound by the weekly scan, unverifiedSep 25, 2026

Sources: each lab's own papers and arXiv, with citation and upvote counts from Semantic Scholar and Hugging Face. One-line summaries are for orientation, not a substitute for the paper. Logos via logo.dev; trademarks belong to their owners.

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