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University of Illinois Urbana-Champaign
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RL fine-tuning can nearly double the performance of LLM agents on complex, multi-step spreadsheet tasks, suggesting a promising path towards automating real-world data-centric workflows.
Traditional research papers are costing AI agents reproducibility and understanding, but a new "Agent-Native" format that captures the full messy research process boosts performance by up to 20%.
