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KARL: Knowledge Agents via Reinforcement Learning
arXiv – CS AI|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|
🤖AI Summary
Researchers present KARL, a reinforcement learning system for training enterprise search agents that outperforms GPT 5.2 and Claude 4.6 on diverse search tasks. The system introduces KARLBench evaluation suite and demonstrates superior cost-quality trade-offs through multi-task training and synthetic data generation.
Key Takeaways
- →KARL achieves state-of-the-art performance on enterprise search tasks, surpassing GPT 5.2 and Claude 4.6.
- →KARLBench introduces six distinct search evaluation regimes including constraint-driven search and cross-document synthesis.
- →Multi-task training across heterogeneous search behaviors provides better generalization than single-task optimization.
- →The system uses iterative bootstrapping with synthetic data generation for training high-quality knowledge agents.
- →KARL demonstrates Pareto-optimal performance across cost-quality and latency-quality trade-offs.
Mentioned in AI
Models
GPT-5OpenAI
ClaudeAnthropic
#reinforcement-learning#enterprise-search#ai-agents#benchmarks#synthetic-data#multi-task-learning#knowledge-agents#search-optimization
Read Original →via arXiv – CS AI
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