[2603.05218] KARL: Knowledge Agents via Reinforcement Learning

Computer Science > Artificial Intelligence arXiv:2603.05218 (cs) [Submitted on 5 Mar 2026] Title:KARL: Knowledge Agents via Reinforcement Learning Authors:Jonathan D. Chang, Andrew Drozdov, Shubham Toshniwal, Owen Oertell, Alexander Trott, Jacob Portes, Abhay Gupta, Pallavi Koppol, Ashutosh Baheti, Sean Kulinski, Ivan Zhou, Irene Dea, Krista Opsahl-Ong, Simon Favreau-Lessard, Sean Owen, Jose Javier Gonzalez Ortiz, Arnav Singhvi, Xabi Andrade, Cindy Wang, Kartik Sreenivasan, Sam Havens, Jialu Liu, Peyton DeNiro, Wen Sun, Michael Bendersky, Jonathan Frankle View a PDF of the paper titled KARL: Knowledge Agents via Reinforcement Learning, by Jonathan D. Chang and Andrew Drozdov and Shubham Toshniwal and Owen Oertell and Alexander Trott and Jacob Portes and Abhay Gupta and Pallavi Koppol and Ashutosh Baheti and Sean Kulinski and Ivan Zhou and Irene Dea and Krista Opsahl-Ong and Simon Favreau-Lessard and Sean Owen and Jose Javier Gonzalez Ortiz and Arnav Singhvi and Xabi Andrade and Cindy Wang and Kartik Sreenivasan and Sam Havens and Jialu Liu and Peyton DeNiro and Wen Sun and Michael Bendersky and Jonathan Frankle View PDF HTML (experimental) Abstract:We present a system for training enterprise search agents via reinforcement learning that achieves state-of-the-art performance across a diverse suite of hard-to-verify agentic search tasks. Our work makes four core contributions. First, we introduce KARLBench, a multi-capability evaluation suite spanning six distinct search reg...