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Reinforced Attention Learning research paper by Google, 2026

Google · Feb 4, 2026 · Reasoning · 3 citations · 30 upvotes · unverified

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What it shows

Reinforced Attention Learning optimizes internal attention distributions in multimodal language models, improving information allocation and cross-modal alignment through policy-gradient methods.

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

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About this paper
Authors
Bangzheng Li, Jianmo Ni, Chen Qu and 5 more
arXiv
2602.04884 · PDF
Venue
arXiv.org
Citations
3, 0 influential · Semantic Scholar
Upvotes
30 · Hugging Face
Lab
Google · on Companies · on Acquisitions · on Paydays · on TechConf · on Releases

Changes

What changed
Influential citationsfirst count: 0Sep 25, 2026
Citationsfirst count: 3Sep 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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