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Showing papers from Harvard University & Amazon Show all papers

71%Highly rated
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NeurIPS 2026HarvardHarvard University & AmazonOptimization

Compute Efficiency and Serial Runtime Tradeoffs for Stochastic Momentum Methods

Heavy ball and ASGD face compute-efficiency versus serial-runtime tradeoffs in linear regression, with heavy ball extending SGD's efficient batch window by up to √κ and ASGD trading small-batch efficiency for runtime on fast-decaying spectra.

Depen Morwani, Alexandru Meterez, Pranav Nair, Sham Kakade

Sydney Poster Session 2, Tue, Dec 8, 5:00 PM–8:00 PM, Hall 1-4 · Published 2026

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7/20 AI panelreviewers recommend it

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AI panel: 7 of 20 reviewers recommend it
lenient 1/5
medium 4/10
strict 2/5
80%Must read
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NeurIPS 2026HarvardCornellMicrosoft ResearchKempner and SEAS at HarvardHarvard University & AmazonRL for LLMs

Scaling Reward Modeling without Human Supervision

Unsupervised reward modeling via web document prefix-suffix preference learning improves RewardBench accuracy up to 7.7 points and matches supervised baselines without human annotations.

Jingxuan Fan, Yueying Li, Zhenting Qi, Dinghuai Zhang and 3 more

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

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12/20 AI panelreviewers recommend it

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AI panel: 12 of 20 reviewers recommend it
lenient 5/5
medium 5/10
strict 2/5
89%Must read
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NeurIPS 2026Harvard University, HarvardHarvardCarnegie MellonGoogle DeepMindGoogle ResearchGenomics & single-cell

How Post-Training Shapes Biological Reasoning Models

Continued pre-training aligns biological language, supervised fine-tuning improves in-domain but harms out-of-domain reasoning, and reinforcement learning recovers generalization when rewards align.

Lukas Fesser, Hanlin Zhang, Michelle M Li, Eric Wang and 4 more

Sydney Poster Session 5, Thu, Dec 10, 10:00 AM–1:00 PM, Hall 1-4 · Published 2026 · ▲ 3 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 7/10
strict 4/5