Michael Y. Li(@michaelyli_):You're wasting FLOPs when scaling inference compute: by independently sampling parallel attempts, you burn compute rediscovering the same solutions. Introducing QuasiMoTTo: we scale parallel sampling with correlated samples instead! These samples have higher coverage, are marginally exact draws from the LLM, and can be generated in parallel. Result: same performance with 25-47% fewer samples in test-time scaling + 50% fewer training steps in RL! In our new paper, we explore the design space of correlated samplers. Work with co-authors @probablynotaz9 (co-lead), @gandhikanishk, @noahdgoodman, and Emily Fox!

Michael Y. Li

@michaelyli_

CS PhD @StanfordAILab @StanfordNLP advised by @noahdgoodman and Emily Fox. Prev: undergrad @princeton

参加 March 2024

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Michael Y. Li@michaelyli_

2026.07.02 17:40

You're wasting FLOPs when scaling inference compute: by independently sampling parallel attempts, you burn compute rediscovering the same solutions. Introducing QuasiMoTTo: we scale parallel sampling with correlated samples instead! These samples have higher coverage, are marginally exact draws from the LLM, and can be generated in parallel. Result: same performance with 25-47% fewer samples in test-time scaling + 50% fewer training steps in RL! In our new paper, we explore the design space of correlated samplers. Work with co-authors @probablynotaz9 (co-lead), @gandhikanishk, @noahdgoodman, and Emily Fox!