Cost-Aware Learning
We consider the problem of Cost-Aware Learning, where sampling different components of a finite-sum objective incurs different costs. The objective is to reach a target error while minimizing the total cost. We propose Cost-Aware SGD, which uses a distribution based on gradient norms and costs to sample components. We provide a thorough analysis of this algorithm, including cost-improvement bounds over baselines, a characterization of distribution proxy sub-optimality, and a lower bound. We apply our theoretical insights to reinforcement learning with language models, where the computational cost of sequence-level policy gradients varies with length. We find that the advantage magnitude s...
| Authors | Clara Mohri, Amir Globerson, Haim Kaplan et al. |
|---|---|
| Primary category | cs.LG |
| Published | 30 April 2026 |
| Last updated at source | 29 May 2026 |
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