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Home/Authors/Michael Y. Li

Michael Y. Li

1 indexed paper

Recent (6 mo)
1
With code
0
Influential cites
0
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Publications per year

1
26

Top categories

ML×1NLP×1

Frequent co-authors

Anthony Zhan1×
Kanishk Gandhi1×
Noah D. Goodman1×
Emily B. Fox1×

Research Timeline

2026
QuasiMoTTo: Quasi-Monte Carlo Test-Time Scaling

This paper introduces QuasiMoTTo, a method for generating correlated but exact samples in parallel to improve sample efficiency in scaling inference compute and reinforcement learning.

Highlighted terms show continued research focus across papers

Papers

cs.LGcs.CLEmpiricalRecentJul 1, 2026

QuasiMoTTo: Quasi-Monte Carlo Test-Time Scaling

Michael Y. Li, Anthony Zhan, Kanishk Gandhi, Noah D. Goodman +1 more

This paper introduces QuasiMoTTo, a method for generating correlated but exact samples in parallel to improve sample efficiency in scaling inference compute and reinforcement learning.

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