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This paper proposes a budget-aware online teaching framework for web agents that reduces teacher calls and compute costs while maintaining performance.
The paper introduces Net Utility, a data-free metric for budget-aware LoRA merging that optimizes rank allocation across tasks, achieving +2.1% improvement on vision tasks and +2.2% on language tasks over uniform methods.
BAP-SQL presents a budget-aware observation planning approach for agentic text-to-SQL, treating SQL query choice as a budget-control decision to improve tight-budget success while reducing token usage.
Proposes FedEAS, a budget-aware policy for synthetic data augmentation in federated learning that assigns each client an entropy-adaptive per-class generation budget, recovering most accuracy gains of full class balancing while reducing generation cost by 94.1%.
Introduces SEVRA, a selective verification controller for budget-aware reasoning that decides when to accept a model's initial answer versus spending extra compute on verification, improving accuracy and reducing unnecessary tokens on benchmarks like MATH500 and GSM8K.