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New research questions excessive sampling in AI models

By PulseAugur Editorial · [6 sources] ·

A new research paper explores the concept of test-time scaling in language models and reasoning systems, arguing that excessive sampling can lead to worse performance. The paper introduces the 'modal ceiling' and 'correlation ceiling' to describe the point at which additional sampling yields diminishing returns or even negative outcomes. It suggests that the bottleneck in these systems is recognizing a correct answer rather than generating one, and that a few dozen draws are sufficient for most tasks. AI

IMPACT Suggests that current methods of increasing AI performance through extensive sampling may be inefficient and counterproductive.

RANK_REASON The cluster contains an academic paper published on arXiv detailing new research findings.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 6 sources. How we write summaries →

New research questions excessive sampling in AI models

COVERAGE [6]

  1. arXiv cs.AI TIER_1 English(EN) · Xuqing Yang, Yi Yuan, Shanzhe Lei, Xuhong Wang ·

    Scaling with Confidence: Calibrating Confidence of LLMs for Adaptive Test Time Scaling

    arXiv:2607.01612v1 Announce Type: new Abstract: Training large language models (LLMs) with reinforcement learning (RL) has significantly advanced their performance on reasoning and question-answering tasks. However, prevailing RL reward designs typically prioritize response corre…

  2. arXiv cs.CL TIER_1 Italiano(IT) · Michael Y. Li, Anthony Zhan, Kanishk Gandhi, Noah D. Goodman, Emily B. Fox ·

    QuasiMoTTo: Quasi-Monte Carlo Test-Time Scaling

    arXiv:2607.01179v1 Announce Type: cross Abstract: Scaling inference compute, by generating many parallel attempts per problem, is a costly but reliable lever for improving language model capabilities. By default these attempts are generated independently, wasting inference comput…

  3. arXiv cs.CL TIER_1 Italiano(IT) · Emily B. Fox ·

    QuasiMoTTo: Quasi-Monte Carlo Test-Time Scaling

    Scaling inference compute, by generating many parallel attempts per problem, is a costly but reliable lever for improving language model capabilities. By default these attempts are generated independently, wasting inference compute on redundant solutions. This waste seems unavoid…

  4. Hugging Face Daily Papers TIER_1 English(EN) ·

    When More Sampling Hurts: The Modal Ceiling and Correlation Ceiling of Test-Time Scaling

    Sampling-based reasoning systems face a trade-off between coverage and selection, where additional samples beyond a few dozen provide diminishing returns and can degrade performance.

  5. arXiv stat.ML TIER_1 English(EN) · Yong Yi Bay, Kathleen A. Yearick ·

    When More Sampling Hurts: The Modal Ceiling and Correlation Ceiling of Test-Time Scaling

    arXiv:2606.28661v1 Announce Type: cross Abstract: People overthink; language models over-sample, and the extra effort can talk both into a worse answer. Reasoning systems answer a hard question by sampling it many times (test-time scaling), and the more they draw, the more often …

  6. arXiv stat.ML TIER_1 English(EN) · Kathleen A. Yearick ·

    When More Sampling Hurts: The Modal Ceiling and Correlation Ceiling of Test-Time Scaling

    People overthink; language models over-sample, and the extra effort can talk both into a worse answer. Reasoning systems answer a hard question by sampling it many times (test-time scaling), and the more they draw, the more often a correct answer turns up somewhere, so coverage, …

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