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Large language model (LLM) agents can benefit from reusable skills distilled from prior task experience, yet existing skill optimization methods often rely on costly execution-based evaluation and substantial task data. We introduce \textbf{COBRA-Skills}, an efficient framework that formulates skill optimization as bud...
This paper proposes algorithms that model online fair division as a contextual bandit problem and achieve provable sublinear regret and proposes algorithms that model utility is an unknown function of item-agent features.
This work describes the near-optimal region, the set of allocations within a specified tolerance of peak performance, which is wide even for small tolerances, widens with model scale, and transfers reliably from small proxy models to large target models.
This work introduces Power-Law Entropy Search (PLES), a computational cost-aware acquisition function built on multi-fidelity Bayesian optimization that efficiently estimates optimal hyperparameter scaling laws through adaptive experimentation using less than one-tenth of the computational budget required by convention...
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