It's clear that 2026 will be the "RL" big year. How AI labs use productive data in real-time (almost) training without comprising user experience , data privacy and evaluate is even a bigger questions. CC is rising from there.
OpenAI's blog () points out that today’s language models hallucinate because training and evaluation reward guessing instead of admitting uncertainty. This raises a natural question: can we reduce hallucination without hurting utility?🤔
On-policy RL with our Binary Retrieval-Augmented Reward (RAR) can improve factuality (40% reduction in hallucination) while preserving model utility (win rate and accuracy) of fully trained, capable LMs like Qwen3-8B.
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