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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.
LLMs still struggle to accurately infer user interests from interaction histories, especially when dealing with diverse engagement signals – a critical gap for effective personalization.
