Online Convex Optimization with Dueling Feedback
Noisy binary comparison between two candidates is a common interface between human and learning systems, especially in modern large language model (LLM) post-training alignment. We study online convex optimization with dueling (pairwise comparison) feedback, where the learner observes only a binary preference between two queried points. We consider adversarial sequences of convex losses and measure regret with the loss at both queried points, under a comparison link with a known nonzero slope at the origin. We propose a simple reduction that converts dueling feedback into approximate gradients, enabling the use of standard first-order methods. We show that regret guarantees transfer under...
| Authors | Yiyang Lu, Hareshkumar Jadav, Mohammad Pedramfar et al. |
|---|---|
| Primary category | cs.LG |
| Published | 15 August 2026 |
| Last updated at source | 28 September 2026 |
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