Rewards for Budget-Aware Multi-Turn Agents That Adapt to Arbitrary Test-Time Constraints

arXiv:2610.28409v1 Announce Type: cross Abstract: 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

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