---
title: "Test-Time Scaling in Reasoning LLMs: Inference Regimes, Evaluation, and Reproducibility"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2608.04001v1"
permalink: "https://feed.astralor.com/items/itm_8c30a3a81cd4bb356b654fc5"
published: "2026-08-04T17:57:20.000Z"
score: 77
channel: "ai"
tags: ["arXiv","Reproducibility","Open-Weight Ecosystem","Reasoning LLMs","Test-time Scaling"]
---

# Test-Time Scaling in Reasoning LLMs: Inference Regimes, Evaluation, and Reproducibility

## 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.

## At a glance

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.

## Key points

- 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

Large language models can solve substantially harder reasoning problems with more inference-time compute. The term "test-time scaling," however, now covers diverse inference algorithms that extend deliberation along a single trajectory, sample completed candidates and aggregate them through voting or verification, or search over unfinished partial states. These algorithms differ in their statistical structure, compute accounting, and failure modes. Treating these procedures as interchangeable under a single scalar "budget," or reporting accuracy without the inference protocol that produced it, makes results difficult to compare across studies. We develop a systematic account of test-time scaling along three axes. First, we formalize test-time scaling as budgeted inference over the implicit prefix tree of an autoregressive model and distinguish three structural regimes: single-trajectory sequential scaling, leaf-level scaling with terminal reduction, and prefix-level scaling. Second, we treat the evaluated object as the entire inference system and develop evaluation principles that separate end-to-end system performance from candidate-bank diagnostics. We introduce an evaluation profile whose coordinates and simple functionals recover or bound common repeated-sampling metrics, and prescribe protocol-matched reporting of compute and uncertainty. Third, we specify reproducibility requirements for inference protocols, distinguishing exact replay from distributional reproducibility and identifying the artifacts needed to support each. We also organize the open-weight reasoning ecosystem by model-side and interface mechanisms, apply these principles to broad-knowledge, symbolic-reasoning, and competition-mathematics benchmarks, and assemble over 2 billion full reasoning traces for release with progressively richer verifier and token-level signals.

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