Tom A. Lamb, Adam Golinski, Eleonora Gualdoni, Sinead Williamson, Michael Kirchhof · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28409 · open-access PDF
大型语言模型代理人经常得到一个推断预算,并被要求最大限度地在其内取得成功。多转型代理人必须在许多环境交互中分配这个预算。外部支撑可以强制执行预算,但仍然不清楚代理人自己是否可以学习何时以及花多少钱。我们询问代理人是否能够在动态定义的预算中学习这个分配,哪些奖励最能教导这种行为。
Machine translation (Meta M2M100); the English original follows.
Large language model agents are often given an inference budget and asked to maximise success within it. A multi-turn agent has to allocate this budget across many environment interactions. An external harness can enforce the budget, but it remains unclear whether the agent itself can learn when and how much to spend. We ask whether agents can learn this allocation across dynamically specified budgets and which rewards best teach this behaviour. We adapt existing length penalties to budget constraints spanning multiple resource types, introduce new reward formulations, and compare eleven objec
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