Test-Time Scaling in Reasoning LLMs: Inference Regimes, Evaluation, and Reproducibility
Score
77首次系统区分了三种测试时扩展机制，并引入评估概况，信息增量显著。
理论框架完整且附带大规模数据集，但作为预印本内容质量仍需同行评审确认。
提供了可直接使用的评估原则和超过20亿条推理轨迹，工程与科研实用价值高。
文章发布于2026年8月，紧扣测试时扩展这一当前热点，时效性强。
来源为arXiv预印本，尚未经过正式同行评审，但机构背景和作者可信度较高。
Base 85 × T2 Source weight ×0.9 = 77
Curation line 70Selected
Why selected
Presents a systematic evaluation framework and a massive dataset with strong originality and practicality, though as a preprint it awaits peer review.
AI commentary
「This paper turns 'test-time scaling' from a buzzword into an actionable framework. If you are comparing reasoning models or planning to reproduce a scaling experiment, the proposed evaluation profile and protocol-matching requirements will become your new checklist. The 2-billion-trace release is especially worth watching.」
Quick overview
Large language models can solve much harder reasoning problems with additional inference-time compute, yet the term 'test-time scaling' now encompasses diverse algorithms that differ in statistical structure, compute accounting, and failure modes. This paper develops a systematic account along three axes: it formalizes test-time scaling as budgeted inference over an autoregressive model's implicit prefix tree, distinguishing single-trajectory, leaf-level, and prefix-level regimes; it treats the whole inference system as the evaluated object, introducing an evaluation profile that recovers or bounds common repeated-sampling metrics and calls for protocol-matched reporting; and it specifies reproducibility requirements separating exact replay from distributional reproducibility. The authors also organize the open-weight reasoning ecosystem and assemble over 2 billion full reasoning traces for release with verifier and token-level signals.
- Formalizes test-time scaling as budgeted inference over an implicit prefix tree, distinguishing single-trajectory, leaf-level, and prefix-level scaling regimes.
- Proposes treating the entire inference system as the evaluation target, unifying repeated-sampling metrics via an evaluation profile and requiring protocol-matched reporting.
- Specifies reproducibility requirements separating exact replay from distributional reproducibility, listing required artifacts.
- Organizes the open-weight reasoning ecosystem and releases over 2 billion complete reasoning traces with verifier and token-level signals.
Full text
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