1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.AI2026
Collaborative Learning and Reasoning over Model Slices
Yuheng Wang, Runde Yang, Zhuohan Bao +9
Large language models (LLMs) excel across diverse tasks, benefiting from knowledge priors, in-context reasoning, and test-time computation. Despite these strengths, single-model re…
cs.LG2026
Not All Negative Samples Are Equal: LLMs Learn Better from Plausible Reasoning
Zixiang Di, Jinyi Han, Shuo Zhang +8
Learning from negative samples holds great promise for improving Large Language Model (LLM) reasoning capability, yet existing methods treat all incorrect responses as equally info…
cs.LG2025★ 1 cited
CURE: Critical-Token-Guided Re-Concatenation for Entropy-Collapse Prevention
Qingbin Li, Rongkun Xue, Jie Wang +8
Recent advances in Reinforcement Learning with Verified Reward (RLVR) have driven the emergence of more sophisticated cognitive behaviors in large language models (LLMs), thereby e…