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Improving Data and Reward Design for Scientific Reasoning in Large Language Models research paper by Microsoft, 2026

Microsoft · Feb 9, 2026 · Reasoning · 4 citations · 44 upvotes · unverified

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What it shows

A large-scale scientific question dataset and post-training pipeline are developed to improve open-ended science question answering through enhanced data processing and reinforcement learning with rubric-guided evaluation.

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

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About this paper
Authors
Zijie Chen, Zhenghao Lin, Xiao Liu and 3 more
arXiv
2602.08321 · PDF
Venue
arXiv.org
Citations
4, 0 influential · Semantic Scholar
Upvotes
44 · Hugging Face
Lab
Microsoft · on Companies · on Acquisitions · on Quarterly · on Paydays · on Releases · on TechConf

Changes

What changed
Influential citationsfirst count: 0Sep 25, 2026
Citationsfirst count: 4Sep 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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