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Exploratory Memory-Augmented LLM Agent via Hybrid On- and Off-Policy Optimization research paper by Microsoft, 2026

Microsoft · Feb 26, 2026 · Agents and evaluation · 18 citations · 36 upvotes · unverified

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What it shows

EMPO² is a hybrid reinforcement learning framework that enhances exploration for large language model agents by integrating memory mechanisms with on- and off-policy updates, demonstrating improved performance and adaptability in complex environments.

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

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About this paper
Authors
Zeyuan Liu, Jeonghye Kim, Xufang Luo and 2 more
arXiv
2602.23008 · PDF
Venue
arXiv.org
Citations
18, 2 influential · Semantic Scholar
Upvotes
36 · Hugging Face
Lab
Microsoft · on Companies · on Acquisitions · on Quarterly · on Paydays · on Releases · on TechConf

Changes

What changed
Influential citationsfirst count: 2Sep 25, 2026
Citationsfirst count: 18Sep 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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