Learning to Retrieve via Reinforcement Learning in Embedding Space
Dense retrieval models are typically trained with contrastive objectives that learn effective representations but do not directly optimize retrieval metrics or downstream task performance. To address this problem, we introduce RELER (REinforcement LEarning for Retrieval), a reinforcement learning framework that enables...
Qi Liu, Feng-Ming Liang, Yi-Qun Chen et al.
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