Databricks' KARL agent uses reinforcement learning to generalize across six enterprise search behaviors — the problem that breaks most RAG pipelines.
A new technical paper titled “Multi-Agent Reinforcement Learning for Microprocessor Design Space Exploration” was published by researchers at Harvard University and Google research groups.
Databricks has released KARL, an RL-trained RAG agent that it says handles all six enterprise search categories at 33% lower ...
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