KARL introduces a reinforcement learning framework that dynamically estimates an LLM's knowledge boundary to reward abstention only when appropriate, achieving a superior accuracy-hallucination trade-off on multiple benchmarks without sacrificing correctness. What the Researchers Built KARL (Knowledge-boundary-Aware Reinforcement Learning) is a new framework designed to teach large language models when to say "I don't know" — without making them overly cautious. The core problem it solves: existing RL methods for hallucination reduction use static reward functions that penalize all incorrect answers equally, causing models to abstain from questions they could actually answer correctly. KARL's innovation is a dynamic reward mechanism that continuously estimates the model's own knowledge boundary during training, then rewards correct answers and guided abstentions appropriately. This prevents the "abstention trap" where models become too conservative. Key Results Metric KARL vs.…