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Showing papers from University of Wisconsin–Madison Show all papers

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arXivU Wisconsin–MadisonNew YorkStanfordRL for LLMs

Sharpening Tax in Post-Training

Post-training sharpens base model behaviors at the cost of solution coverage, introducing a quantifiable "Sharpening Tax"; a posterior-tempered group sampler reduces this tax while boosting accuracy.

Changdae Oh, Qi Zeng, Qi Qi, Andrey Zhmoginov and 6 more

Published Oct 1, 2026 · 0 citations · ▲ 102 on Hugging Face · Code ★ 25

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17/20 AI panelreviewers recommend it

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AI panel: 17 of 20 reviewers recommend it
lenient 4/5
medium 10/10
strict 3/5
89%Must read
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arXivShanghai Jiao TongAlibaba GroupU Wisconsin–MadisonU Illinois Urbana-ChampaignMila - Quebec Artificial IntelliLLM pretraining & scaling laws

OPUS: Towards Efficient and Principled Data Selection in Large Language Model Pre-training in Every Iteration

OPUS defines optimizer-induced update-space data utility for dynamic LLM pre-training selection, outperforming full-scale baselines with minimal overhead.

Shaobo Wang, Xuan Ouyang, Tianyi Xu, Yuzheng Hu and 8 more

Published Feb 5, 2026 · 0 citations · ▲ 354 on Hugging Face

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16/20 AI panelreviewers recommend it

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AI panel: 16 of 20 reviewers recommend it
lenient 5/5
medium 9/10
strict 2/5
80%Must read
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arXivPekingNanyang TechnologicalTsinghuaU Wisconsin–MadisonPrincetonMultimodal reasoning

BabyVision: Visual Reasoning Beyond Language

BabyVision benchmarks core visual reasoning without language and finds top MLLMs score far below human children.

Liang Chen, Weichu Xie, Yiyan Liang, Hongfeng He and 26 more

Published Jan 10, 2026 · 1 citation · ▲ 201 on Hugging Face · Code ★ 257

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12/20 AI panelreviewers recommend it

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AI panel: 12 of 20 reviewers recommend it
lenient 4/5
medium 7/10
strict 1/5
45%Niche pick
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NeurIPS 2026U Wisconsin–MadisonU Wisconsin - MadisonDeep RL

When Does Knowing the State Help? Diagnosing Process vs. Outcome Reward Design

Wenpei Shao, Ross Jacobucci

Atlanta Poster Session 1, Wed, Dec 9, 10:00 AM–1:00 PM, Hall C1 · Published 2026

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0/20 AI panelreviewers recommend it

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AI panel: 0 of 20 reviewers recommend it
lenient 0/5
medium 0/10
strict 0/5
83%Must read
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NeurIPS 2026U Wisconsin-MadisonU Wisconsin–MadisonEfficient inference & serving

Scalable Derivative Gaussian Processes via Exact Gradient Reduction

TERA introduces exact gradient reduction for derivative GPs, reducing inference cost to O(dm²+m⁶) per target with flat scaling in dimension d while preserving the model and improving predictive accuracy.

Hyunseok Seung, Matthias Katzfuss

Atlanta Poster Session 2, Wed, Dec 9, 4:30 PM–7:30 PM, Hall C1 · Published 2026

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13/20 AI panelreviewers recommend it

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AI panel: 13 of 20 reviewers recommend it
lenient 3/5
medium 8/10
strict 2/5