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SERL-SQL: Selective Hindsight Distillation for Text-to-SQL Reinforcement Agentic Learning
Recent Text-to-SQL systems increasingly rely on multi-turn interaction, execution feedback, and reinforcement learning. However, most existing methods use execution correctness only as a trajectory-level reward, which provides limited guidance for identifying the SQL decisions responsible for succes
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