Efficient Cost-Aware LLM Evaluation via Bayesian Bandit Gittins Indices
Exhaustively evaluating every candidate LLM configuration on every benchmark item to identify a high-performing one is costly. We formulate configuration selection as a cost-aware Bayesian bandit problem and propose GittinsEval, which draws on the Bayesian-optimal Gittins policy to determine which configuration to evaluate next and when to stop. We extend the policy with an anytime recommendation rule over both fully and partially evaluated configurations, using an LCB-style score to account for posterior uncertainty. GittinsEval is computationally efficient, requiring only lightweight online updates after offline precomputation. Across GSM8K, PIQA, AlpacaEval, and MMLU response matrices,...
| Authors | Qian Xie, Yueli He, Nairen Cao |
|---|---|
| Primary category | cs.LG |
| Published | 22 September 2026 |
| Last updated at source | 5 October 2026 |
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