# Contextual Slate GLM Bandits with Limited Adaptivity

arXiv: https://arxiv.org/abs/2606.31449 · HTML: https://mlsanity.com/paper/2606.31449/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 35 references that are in the map.
- First posted on arXiv 30 Jun 2026.

## Builds on (most cited first, 20 of 20)

- [Nomic Embed: Training a Reproducible Long Context Text Embedder](https://mlsanity.com/paper/2402.01613/) (2024)
- [Improved Optimistic Algorithms for Logistic Bandits](https://mlsanity.com/paper/2002.07530/) (2020)
- [Batched Multi-armed Bandits Problem](https://mlsanity.com/paper/1904.01763/) (2019)
- [Mostly Exploration-Free Algorithms for Contextual Bandits](https://mlsanity.com/paper/1704.09011/) (2017)
- [Online Learning with Switching Costs and Other Adaptive Adversaries](https://mlsanity.com/paper/1302.4387/) (2013)
- [Linear Bandits with Limited Adaptivity and Learning Distributional Optimal Design](https://mlsanity.com/paper/2007.01980/) (2020)
- [Jointly Efficient and Optimal Algorithms for Logistic Bandits](https://mlsanity.com/paper/2201.01985/) (2022)
- [An Efficient Bandit Algorithm for Realtime Multivariate Optimization](https://mlsanity.com/paper/1810.09558/) (2018)
- [Leveraging Good Representations in Linear Contextual Bandits](https://mlsanity.com/paper/2104.03781/) (2021)
- [Multiple-Play Bandits in the Position-Based Model](https://mlsanity.com/paper/1606.02448/) (2016)
- [OSOM: A simultaneously optimal algorithm for multi-armed and linear contextual bandits](https://mlsanity.com/paper/1905.10040/) (2019)
- [Generalized Linear Bandits with Limited Adaptivity](https://mlsanity.com/paper/2404.06831/) (2024)
- [The Externalities of Exploration and How Data Diversity Helps Exploitation](https://mlsanity.com/paper/1806.00543/) (2018)
- [Automated Creative Optimization for E-Commerce Advertising](https://mlsanity.com/paper/2103.00436/) (2021)
- [On largest volume simplices and sub-determinants](https://mlsanity.com/paper/1406.3512/) (2014)
- [Algorithms for slate bandits with non-separable reward functions](https://mlsanity.com/paper/2004.09957/) (2020)
- [Linear Contextual Bandits with Hybrid Payoff: Revisited](https://mlsanity.com/paper/2406.10131/) (2024)
- [Efficient Algorithms for Logistic Contextual Slate Bandits with Bandit Feedback](https://mlsanity.com/paper/2506.13163/) (2025)
- [Achieving Limited Adaptivity for Multinomial Logistic Bandits](https://mlsanity.com/paper/2508.03072/) (2025)
- [Practical and Optimal Algorithm for Linear Contextual Bandits with Rare Parameter Updates](https://mlsanity.com/paper/2606.00984/) (2026)

## Abstract (arXiv)

We investigate the contextual slate bandit problem with generalized linear rewards under limited adaptivity. At each round, the learner is presented with N sets of items, where each item is represented by a d-dimensional feature vector. The learner then constructs a slate by selecting one item per set; the resulting slate yields a scalar reward sampled from a Generalized Linear Model (GLM). We propose algorithms under two limited-adaptivity settings: (a) Batched and (b) Rarely-Switching. For the batched setting, we introduce B-SlateGLinCB, which partitions the time horizon into O(loglog T) batches such that each batch's policy relies only on data from previous batches. For the rarely-switching setting, we propose RS-SlateGLinCB, which adaptively performs only O(Ndlog T) parameter updates. Under a diversity assumption on the item sequences, we prove that B-SlateGLinCB and RS-SlateGLinCB achieve regret bounds of O(Nd3/2√T) and O(Nd√T), respectively. Notably, both bounds are independent of the non-linearity parameter kappa that is typically found to scale the regret of GLM bandit algorithms. Our algorithms are computationally efficient, requiring only poly(N) time per round despite 2Omega(N) possible slates. Simulations show our algorithms outperform existing baselines with limited adaptivity and remain competitive with Slate-GLM-OFU, a fully adaptive state-of-the-art algorithm. Notably, a slightly modified B-SlateGLinCB empirically matches this baseline. Finally, we demonstrate strong performance in a practical in-context example selection task for language models.