Correlation-Aware Contextual Bandits with Surrogate Rewards for LLM Routing
We study contextual bandit problems with correlated arms and access to surrogate reward signals produced by a machine learning model, motivated by applications such as large language model (LLM) routing. Unlike classical contextual bandits that rely solely on bandit feedback and assume conditional independence across arms, our setting allows context-dependent inter-arm correlations and auxiliary reward information that may be noisy or misspecified. We propose algorithms that leverage such surrogate rewards through two complementary designs. A coupled reward-mixing approach pools true and surrogate rewards to accelerate learning when surrogate signals are reliable, while a decoupled predic...
| Authors | Ajay Narayanan Sridhar, Ronak Singh, Mehrdad Mahdavi et al. |
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| Primary category | cs.LG |
| Published | 10 July 2026 |
| Last updated at source | 13 October 2026 |
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