Experiments show that C-MIG outperforms RAGRL baselines in and out of domain and stays competitive with far larger medical-specialist and general-purpose LLMs, at only about 3.4% training overhead and no inference-time cost.
Yu-Wei Miao, Gen Li, Yunsheng Zeng et al.· arXiv.org· 0 citations
GRADE (GRAdient Dynamics for knowlEdge gap detection), which quantifies the knowledge gap via the cross-layer rank ratio of the gradient to that of the corresponding hidden state subspace, motivated by the property of gradients as estimators of the required knowledge updates for a given target.
Yujing Wang, Yuanbang Liang, Yu-Kun Lai et al.· arXiv.org· 1 citation
G-CARL, a grounded, checklist-aligned reinforcement learning framework that combines multi-source retrieval for atomic claim verification with context-aware, instance-specific weighted checklists for response coverage, providing structured supervision for factuality, user-demand satisfaction, and expression quality wit...
Shiao Xie, Siyu Chen, Jian-Wei Lv et al.· 0 citations
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