这篇论文告诉你，在小模型上用测试时扩展，关键是让模型把答案写出来，而不是复杂搜索。Qwen2.5-VL调参后性价比很高。
研究测试时扩展(TTS)是否适用于小规模开源视觉语言模型，使用EXAMS-V多语言视觉多项选择基准。实验对比self-consistency、describe-then-reason结合PRM引导束搜索等方法，基于Qwen2.5-VL-7B-Instruct和Qwen3.5-4B。关键因素是链的可解析性：提示格式导致链正确但不输出答案，加入答案提示和修复步骤后改善。每链token上限从1k升至2k恢复3.7个百分点，但增加链数从8到16仅提升0.15个百分点。PRM束搜索落后普通self-consistency 0.39个百分点且成本高8倍，最佳配置在ImageCLEF 2026测试集达84.1%并排名第一。
Test-Time Scaling for Small VLMs on Multilingual Visual MCQ
Test-time scaling (TTS) reliably improves reasoning in large language models, but whether it transfers to small open vision-language models remains unclear. We examine this on EXAMS-V, a multilingual visual multiple-choice benchmark, comparing self-consistency, describe-then-reason with PRM-guided beam search, and two post-hoc selectors across Qwen2.5-VL-7B-Instruct and Qwen3.5-4B. What matters is the conditions under which TTS runs, not the search or verification machinery. The largest factor is parseability: an early prompt format left many chains reasoning correctly yet never committing to an answer letter, which a standard answer cue and a guided repair step largely remove. A larger decoding budget removes the rest: raising the per-chain token limit from 1k to 2k recovers 3.7 pp, whereas sampling more chains (8 to 16) adds only 0.15 pp. Once chains have room to finish, elaborate methods contribute little: PRM-guided beam search trails plain self-consistency by 0.39 pp at over eight times the cost, and neither a training-free generative critic nor a trained multimodal PRM beats majority vote across both policies. The largest gain comes instead from the policy model itself (+11.4 pp). Our best configuration reaches 84.1% on the held-out ImageCLEF 2026 test split, ranking first on the Visual MCQ leaderboard.