# The Surrogate Is Not the Reward: Post-Surrogate Primary-Outcome Acquisition in Contextual Bandits

arXiv: https://arxiv.org/abs/2610.06610 · HTML: https://mlsanity.com/paper/2610.06610/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 35 references that are in the map.
- First posted on arXiv 5 Oct 2026.

## Builds on (most cited first, 9 of 9)

- [Best Arm Identification with LLM Judges and Limited Human](https://mlsanity.com/paper/2601.21471/) (2026)
- [Prediction-Guided Active Experiments](https://mlsanity.com/paper/2411.12036/) (2024)
- [Pure Exploration Beyond Reward Feedback: The Role of Post-Action Context](https://mlsanity.com/paper/2502.03061/) (2025)
- [Stratified Sampling for Model-Assisted Estimation with Surrogate Outcomes](https://mlsanity.com/paper/2602.12992/) (2026)
- [Correlation-Aware Contextual Bandits with Surrogate Rewards for LLM Routing](https://mlsanity.com/paper/2607.09015/) (2026)
- [Evaluating Surrogates in Individualized Treatment Rules](https://mlsanity.com/paper/2512.00405/) (2025)
- [Active Multiple-Prediction-Powered Inference](https://mlsanity.com/paper/2605.08429/) (2026)
- [Surrogate-assisted optimal sampling for risk prediction under measurement constraints](https://mlsanity.com/paper/2606.03477/) (2026)
- [Prediction-Powered Active Testing](https://mlsanity.com/paper/2607.08347/) (2026)

## Built on by (most cited first, 1 of 1)

- [Gaussian Process Bandits for AI System Optimization with Selectively Verified Feedback](https://mlsanity.com/paper/2610.17760/) (2026)

## Abstract (arXiv)

We study contextual bandits in which a surrogate is observed after the action but before the learner decides whether to acquire the primary outcome that defines action value and regret. The value of acquiring the primary outcome depends on both decision relevance (how much the current outcome matters for comparing policies) and the residual uncertainty after observing the surrogate. The Audited Surrogate Bandit (ASB) learns a contextual policy while allocating a budget of B primary-outcome acquisitions over T rounds. ASB sets a pre-surrogate acquisition level from current decision relevance and, after observing the surrogate, redistributes that level using an estimate of that residual uncertainty. For a finite class of N policies over K actions, ASB incurs widetilde O[√KTlog N1+√T/B] regret relative to the best policy in the class. In a two-action family where the surrogate does not reveal the better action, a learner that observes the surrogate before deciding whether to acquire can achieve bounded regret, whereas any learner that must decide before seeing the surrogate incurs Omega(T/B) worst-case regret under the same budget. Synthetic experiments show that both acquisition factors matter: ASB has lower regret than variants using only decision relevance or only residual uncertainty. On a KuaiRec benchmark of user-video interactions, the regret gap relative to relevance-only acquisition widens and then narrows as the budget grows.