The agentic system with teacher-bandit planning and distillation to a student model reduces latency by 23% while achieving 89% plan replication accuracy and 15x faster inference on NYC Taxi and IMDB datasets.
Smith et al., ”Runtime adaptive query processing with reinforcement learning,” inProc
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Agentic Cost-Aware Query Planning with Knowledge Distillation for Big Data Analytics
The agentic system with teacher-bandit planning and distillation to a student model reduces latency by 23% while achieving 89% plan replication accuracy and 15x faster inference on NYC Taxi and IMDB datasets.