235 citations · 370 across the 31 of their papers we have counts for
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cs.LG2026
Learn Now, Use Next, Trust Later: Prequential Test-Time Learning for LLM Agents
Tong Zhao, Reed Li, Yuyang Hu +5
Adapting large language model agents during deployment requires not only retaining past experience, but also turning new observations into timely guidance. Many test-time learning…
cs.LG2026
Parameter Importance is Not Static: Evolving Parameter Isolation for Supervised Fine-Tuning
Zekai Lin, Chao Xue, Di Liang +8
Supervised Fine-Tuning (SFT) of large language models often suffers from task interference and catastrophic forgetting. Recent approaches alleviate this issue by isolating task-cri…
cs.LG2025
DuPO: Enabling Reliable LLM Self-Verification via Dual Preference Optimization
Shuaijie She, Yu Bao, Yu Lu +7
We present DuPO, a dual learning-based preference optimization framework that generates annotation-free feedback via a generalized duality. DuPO addresses two key limitations: Rein…