Don't Let Bandit Feedback Pull Continual LLM-Recommender Updates Off Target
Generative LLM-based recommenders (LLM-Rec) require continual post-deployment updates, yet deployment logs provide only policy-shaped contextual bandit feedback: outcomes are observed solely for items exposed by a prior serving policy, inducing exposure bias and yielding partial, asymmetric signals consisting of relatively reliable positive responses and ambiguous no-responses. We propose an Anchored Bandit Policy Optimization (ABPO) framework for continual LLM-Rec updates that combines group-relative policy optimization (GRPO) with explicit treatment of exposure bias and feedback ambiguity. Specifically, we insert the exposed recommendation as a logged anchor into each GRPO rollout group...
| Authors | Taesan Kim, Hyeongjun Yun, Jaegul Choo et al. |
|---|---|
| Primary category | cs.LG |
| Published | 17 May 2026 |
| Last updated at source | 17 May 2026 |
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