13 citations · 13 across the 4 of their papers we have counts for
4 papers
Why Distillation can Outperform Zero-RL: The Role of Flexible Reasoning
Xiao Hu, Xingyu Lu, Liyuan Mao +6
Reinforcement learning (RL) has played an important role in improving the reasoning ability of large language models (LLMs). Some studies apply RL directly to \textit{smaller} base…
Kwai-STaR: Transform LLMs into State-Transition Reasoners
Xingyu Lu, Yuhang Hu, Changyi Liu +12
Mathematical reasoning presents a significant challenge to the cognitive capabilities of LLMs. Various methods have been proposed to enhance the mathematical ability of LLMs. Howev…
EVLM: An Efficient Vision-Language Model for Visual Understanding
Kaibing Chen, Dong Shen, Hanwen Zhong +14
In the field of multi-modal language models, the majority of methods are built on an architecture similar to LLaVA. These models use a single-layer ViT feature as a visual prompt,…
Rethinking Knowledge Distillation via Cross-Entropy
Zhendong Yang, Zhe Li, Yuan Gong +4
Knowledge Distillation (KD) has developed extensively and boosted various tasks. The classical KD method adds the KD loss to the original cross-entropy (CE) loss. We try to decompo…