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Practical and Optimal Algorithm for Linear Contextual Bandits with Rare Parameter Updates

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arXiv:2606. 00984v1 Announce Type: new Abstract: We study linear contextual bandits under rare parameter updates: the learner may incorporate reward feedback into its parameter estimate only at a small number of update times, while still observing contexts online and selecting actions sequentially.

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Authors: Sanghoon Yu, Min-hwan Oh
Released: 2026-06-02 (2mo ago)
Topic: Related to current project
Relation to my work: Read next because Practical and Optimal Algorithm for Linear Contextual Bandits with Rare Parameter Updates overlaps with clean result "LoRA persona trained on <A> alone emits <B> at 23.5% when a co-trained partner learns <A>...<B>, vs 0% control on Qwen2.5-7B-Instruct (MODERATE confidence)", clean result "Leakage rate is a usable signal for recovering trigger-shaped phrases on Gaperon-1125-1B without knowing the hidden trigger itself (MODERATE confidence)", clean result "Language-mismatch LoRA SFT on Qwen2.5-7B leaks the trained completion language into bystander directives the model was never trained on, absent under same-language SFT (LOW confidence)". Matching terms: text, under, line, rate, factor, contexts. Source: arxiv stat.ML (Machine Learning).
arXiv: 2606.00984

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