Learning from What You Retrieve: Online RL Fine-Tuning for Semantic Retrieval
In large-scale e-commerce retrieval, dual-encoder retrievers are op- timized for contrastive similarity, whereas downstream rerankers capture finer-grained relevance preferences; this objective mis- match limits end-to-end retrieval quality. Reinforcement Learning offers a way to use reward-model feedback for retriever adaptation, but we observe that standard policy-gradient updates can degrade embedding geometry, especially when the document index must remain frozen due to industrial constraints. To address this, we propose PAO (Positive-Advantage-Only), a selective RL optimization method. Our analysis reveals that in- discriminate penalization of negative samples (pushing away) in a fro...
| Authors | Shaowei Wei, Chong Huang, Songtao Fang et al. |
|---|---|
| Primary category | cs.IR |
| Published | 31 August 2026 |
| Last updated at source | 31 August 2026 |
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