测试时扩展能提升视觉语言模型推理能力，但效果因模型和任务而异。
Limits and Gains of Test-Time Scaling in Vision-Language Reasoning
- 通过测试时增加计算量，采用结构化推理与迭代自修正
- 闭源模型普遍受益，开源模型仅外部验证有效且迭代可能退化
- 多步推理任务提升明显，感知类任务增益有限，需适配策略
测试时扩展（TTS）作为提升大语言模型推理能力的有力范式，通过在推理阶段分配额外计算实现性能增强，但其在视觉语言模型（VLMs）中的应用仍缺乏系统研究。本文对开源与闭源VLM在不同基准上的推理方法进行了系统性实证分析。结果表明：闭源模型持续受益于结构化推理与迭代自修正；而开源VLM表现不一致——外部验证带来最可靠提升，迭代精炼常导致性能下降。此外，TTS效果具有数据集依赖性，在多步推理任务中显著提升，但在以感知为核心的基准上增益有限。研究揭示了TTS并非通用解法，必须结合模型能力和任务特性进行定制，推动未来自适应TTS策略与多模态奖励模型的发展。
原文摘要 · Abstract (English)
Test-time scaling (TTS) has emerged as a powerful paradigm for improving the reasoning ability of Large Language Models (LLMs) by allocating additional computation at inference, yet its application to multimodal systems such as Vision-Language Models (VLMs) remains underexplored. In this work, we present a systematic empirical study of inference time reasoning methods applied across both open-source and closed-source VLMs on different benchmarks. Our results reveal that while closed-source models consistently benefit from structured reasoning and iterative Self-Refinement, open-source VLMs show inconsistent behavior: external verification provides the most reliable gains, whereas iterative refinement often degrades performance. We further find that the effectiveness of TTS is dataset-dependent, yielding clear improvements on multi-step reasoning tasks but offering only limited gains on perception-focused benchmarks. These findings demonstrate that TTS is not a universal solution and must be tailored to both model capabilities and task characteristics, motivating future work on adaptive TTS strategies and multimodal reward models.
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