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KARL: Knowledge Agents via Reinforcement Learning

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
Main:29 Pages
50 Figures
Bibliography:3 Pages
18 Tables
Appendix:45 Pages
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 regimes, including constraint-driven entity search, cross-document report synthesis, tabular numerical reasoning, exhaustive entity retrieval, procedural reasoning over technical documentation, and fact aggregation over internal enterprise notes. Second, we show that models trained across heterogeneous search behaviors generalize substantially better than those optimized for any single benchmark. Third, we develop an agentic synthesis pipeline that employs long-horizon reasoning and tool use to generate diverse, grounded, and high-quality training data, with iterative bootstrapping from increasingly capable models. Fourth, we propose a new post-training paradigm based on iterative large-batch off-policy RL that is sample efficient, robust to train-inference engine discrepancies, and naturally extends to multi-task training with out-of-distribution generalization. Compared to Claude 4.6 and GPT 5.2, KARL is Pareto-optimal on KARLBench across cost-quality and latency-quality trade-offs, including tasks that were out-of-distribution during training. With sufficient test-time compute, it surpasses the strongest closed models. These results show that tailored synthetic data in combination with multi-task reinforcement learning enables cost-efficient and high-performing knowledge agents for grounded reasoning.

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