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Behavior Quotient Learning for Low-Rank Adaptation of LLM Agents

Zhou, Pengyang et al.
arXiv (OAI Expanded) · Papers
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machine-learning
LLM-based agents rely on heterogeneous interaction capabilities to accomplish complex tasks. Existing approaches often distribute these capabilities across multiple LoRA adapters, which increases adapter storage requirements and introduces routing overhead during inference. A single LoRA avoids this
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