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This research introduces Residual Quantization (RQ) as a novel representation layer for contextual bandits, effectively bridging the gap between linear efficiency and nonlinear expressivity. By utilizing an offline-trained codebook with dynamic shadow mechanisms, the method achieves superior performance across 13 datasets while reducing memory requirements by up to 1000 times compared to standard neural or tree-based baselines. The approach offers a significant advancement in online learning architectures that require strictly bounded memory without sacrificing predictive accuracy.
Bandits via Additive Quantized Representations
arXiv:2610.02440v1 Announce Type: new Abstract: Contextual bandits require balancing nonlinear reward modeling with online efficiency. Tree ensembles and neural methods capture nonlinearities but require periodic retraining and large replay buffers. Linear models update efficiently per observation with O(1) memory, but are fundamentally restricted to linear reward structures. We propose Residual Quantization (RQ) as a representation layer to bridge this gap. An offline-trained RQ codebook maps continuous contexts into discrete centroid assignments across multiple levels, set dynamically through a shadow mechanism. This enables a spectrum of additive bandit algorithms that achieve nonlinear expressivity with strictly bounded memory. Across 13 datasets, RQ variants beat their non-RQ counterparts on 11 of 13 datasets, often by wide margins, while matching doubling-retrain XGBoost and neural baselines using up to 1000 times less memory.
Abstract:Contextual bandits require balancing nonlinear reward modeling with online efficiency. Tree ensembles and neural methods capture nonlinearities but require periodic retraining and large replay buffers. Linear models update efficiently per observation with O(1) memory, but are fundamentally restricted to linear reward structures. We propose Residual Quantization (RQ) as a representation layer to bridge this gap. An offline-trained RQ codebook maps continuous contexts into discrete centroid assignments across multiple levels, set dynamically through a shadow mechanism. This enables a spectrum of additive bandit algorithms that achieve nonlinear expressivity with strictly bounded memory. Across 13 datasets, RQ variants beat their non-RQ counterparts on 11 of 13 datasets, often by wide margins, while matching doubling-retrain XGBoost and neural baselines using up to 1000 times less memory.
40 pages, 16 figures, 12 tables. Accepted at NeurIPS 2026
Machine Learning (cs.LG)
arXiv:2610.02440 [cs.LG]
(or arXiv:2610.02440v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.02440 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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