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cs.CL2024
Flow-DPO: Improving LLM Mathematical Reasoning through Online Multi-Agent Learning
Yihe Deng, Paul Mineiro
Mathematical reasoning is a crucial capability for Large Language Models (LLMs), yet generating detailed and accurate reasoning traces remains a significant challenge. This paper i…
cs.CL2024
Aligning LLM Agents by Learning Latent Preference from User Edits
Ge Gao, Alexey Taymanov, Eduardo Salinas +2
We study interactive learning of LLM-based language agents based on user edits made to the agent's output. In a typical setting such as writing assistants, the user interacts with…