EnumGRPO is a self-improving optimizer for agentic query execution that reduces LLM-operator costs by ~317x while improving accuracy by 18% over a hybrid baseline across four databases.
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SPA trains LLMs via plan-aware RL with adaptive reward shaping and self-improvement on slowdowns to produce faster query rewrites than rule-based or standard LLM methods on IID and OOD workloads.
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Cost-Aware Optimization for Agentic Query Execution
EnumGRPO is a self-improving optimizer for agentic query execution that reduces LLM-operator costs by ~317x while improving accuracy by 18% over a hybrid baseline across four databases.
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SPA: A SQL-Plan-Aware Reinforcement Learning Framework for Query Rewriting with LLMs
SPA trains LLMs via plan-aware RL with adaptive reward shaping and self-improvement on slowdowns to produce faster query rewrites than rule-based or standard LLM methods on IID and OOD workloads.