Researchers have introduced Fiber Regulation Theory (FRT) and Fiber Acquisition Theory (FAT) to describe how learners manage and renew their internal structures during learning. FRT focuses on preserving existing functions and available learning responses, while FAT details the creation and updating of trainable structures, including access to learning signals and functional directions. These two operations are interdependent, with repair obligations influencing learning channels and acquired features enabling new repairs or reducing costs. A practical implementation using Qwen2.5-32B with AdamW demonstrated that coordinating these operations yielded superior performance in terms of exact match accuracy and negative log-likelihood compared to using either operation in isolation. AI
IMPACT Introduces novel theoretical frameworks for understanding and improving model learning dynamics.
RANK_REASON Academic paper introducing new theories and experimental results. [lever_c_demoted from research: ic=1 ai=1.0]
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