事実関係
arXiv:2608.07968v1 Announce Type: new Abstract: Reasoning language models increasingly use test-time compute to improve performance, but existing evaluations typically study this compute one question at a time. Yet when
解説と影響
推理模型的“用力过猛”与“后劲不足” 大型语言模型通过“测试时计算”（Test-Time Compute）——即在回答问题前投入更多算力进行链式思考——来提升表现，这已成为行业共识。然而，一篇来自arXiv的预印本论文（arXiv:2608.07968）揭示了一个被忽视的缺陷：现有的评估方式通常孤立地看待每个问题，而现实场景中，模型面对的是难度各异的一系列问题。研究发现，当把算力视为一种有限预算，需要在多道题目间进行分配时，这些推理模型的表现并不理想。
缺乏算力分配的“全局观” 论文的核心发现在于，推理模型缺乏一种“元认知”能力，即无法准确判断一道题目的难易程度，并据此决定投入多少思考时间。这导致了一种低效的分配模式：模型可能在简单问题上“用力过猛”，消耗了大量不必要的计算资源，而在真正需要深度推理的复杂问题上却“后劲不足”，因预算耗尽而无法充分思考。这种不合理的分配策略，使得模型在固定总计算预算下的整体答题准确率，远低于理想状态下的最优分配方案。
从单题优化到系统效率的视角转换 这项研究将关注点从“如何让模型在一道题上表现更好”转向了“如何让模型在一组题上整体表现最佳”。原文指出，现有的测试时计算方法大多是为单次查询设计的，没有考虑跨问题的资源调配。在算力成本高昂的实际应用中，这种“会思考，但不算计”的特性可能造成巨大的资源浪费。该研究为未来开发更高效、更具自适应能力的推理系统提供了新的方向，即模型不仅需要学会深度思考，更需要学会何时停止思考。
参考資料
出典原文
arXiv:2608.07968v1 Announce Type: new Abstract: Reasoning language models increasingly use test-time compute to improve performance, but existing evaluations typically study this compute one question at a time. Yet when multiple problems share an end-to-end cost or latency constraint, models must decide how to divide limited inference compute among them. We introduce an exam-style evaluation framework for studying this setting, in which a model must distribute one shared token budget across questions with different difficulty and point values to maximize its total score. Across several open and frontier reasoning models, we find that models fail to allocate a shared budget strategically across questions of varying difficulties and values. Models behave largely as greedy sequential solvers: they prioritize questions by presentation order, front-load effort on early questions, and remain insensitive to value, with these tendencies becoming more pronounced as the number of questions grows. Explicit planning prompts spread compute more evenly but do not produce value- or difficulty-aware prioritization. The same behavioral pattern extends from mathematical to code reasoning. These findings establish global budget allocation as a distinct capability that is not captured by conventional per-question evaluation and remains a challenge for current reasoning models.