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KARL: Knowledge Agents via Reinforcement Learning
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KARL: Knowledge Agents via Reinforcement Learning

#KARL #knowledge agents #reinforcement learning #AI framework #decision-making #robotics #automated planning

📌 Key Takeaways

  • KARL is a new AI framework combining knowledge agents with reinforcement learning.
  • It aims to enhance AI decision-making by integrating structured knowledge into learning processes.
  • The approach could improve efficiency in complex tasks requiring both reasoning and adaptation.
  • Research suggests potential applications in areas like robotics, gaming, and automated planning.

📖 Full Retelling

arXiv:2603.05218v1 Announce Type: new 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

🏷️ Themes

Artificial Intelligence, Machine Learning

📚 Related People & Topics

KARL

Radio station in Tracy–Marshall, Minnesota

KARL (105.1 FM) is a radio station broadcasting a Classic Country format in Marshall, Minnesota (licensed to Tracy). The station is currently owned by Linder Radio Group. It carries Westwood One's "Real Country" music format satellite network, but prior to May 2019, it carried Westwood One's "Hot Co...

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Reinforcement learning

Reinforcement learning

Field of machine learning

In machine learning and optimal control, reinforcement learning (RL) is concerned with how an intelligent agent should take actions in a dynamic environment in order to maximize a reward signal. Reinforcement learning is one of the three basic machine learning paradigms, alongside supervised learnin...

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Mentioned Entities

KARL

Radio station in Tracy–Marshall, Minnesota

 Reinforcement learning

Reinforcement learning

Field of machine learning
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Original Source
--> Computer Science > Artificial Intelligence arXiv:2603.05218 [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 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 u...
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arxiv.org

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