Saturation-Insensitive Dueling Bandits with General Function Approximation
We study contextual dueling bandits with general function approximation under the Bradley-Terry-Luce (BTL) preference model. A key challenge in this setting is the saturation of the preference model: when the current reward model can already distinguish two actions with high confidence, the resulting preference feedback becomes weakly informative, making it difficult to further improve reward estimation. Consequently, existing sample-complexity analyses often depend on the inverse-derivative factor $1 / σ'[Δ_{r^\ast}]$ which can be prohibitively large when the link function $σ$ saturates for large reward gaps $Δ_{r^\ast}$. To address this issue, we introduce `SI-CDB`, an algorithm that se...
| Authors | Chenggong Zhang, Xuheng Li, Qiwei Di et al. |
|---|---|
| Primary category | cs.LG |
| Published | 26 September 2026 |
| Last updated at source | 26 September 2026 |
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