KARL uses RL and synthetic data to outperform Claude and GPT on reasoning benchmarks.
Reasoning agents need more than just scale to succeed.
KARL introduces a reinforcement learning framework that leverages synthetic data and iterative off policy training to build grounded reasoning agents.
By hitting Pareto optimal performance in cost and latency, it manages to outperform industry leaders like Claude and GPT on complex benchmarks.
Paper -> KARL: Knowledge Agents via Reinforcement Learning
Posted Mar 6, 2026, 5:30 PM UTC
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