5 citations · 5 across the 15 of their papers we have counts for
4 papers · 1 filter
Rethinking Local Learning: A Cheaper and Faster Recipe for LLM Post-Training
Hengyu Shi, Tianyang Han, Peizhe Wang +3
LLM post-training typically propagates task gradients through the full depth of the model. Although this end-to-end structure is simple and general, it couples task adaptation to f…
Correct Is Not Enough: Training Reasoning Planners with Executor-Grounded Rewards
Tianyang Han, Hengyu Shi, Junjie Hu +3
Reinforcement learning with verifiable rewards has become a common way to improve explicit reasoning in large language models, but final-answer correctness alone does not reveal wh…
Replacement Learning: Training Neural Networks with Fewer Parameters
Yuming Zhang, Peizhe Wang, Tianyang Han +5
End-to-end training with full-depth backpropagation remains the dominant paradigm for optimizing deep neural networks, but its efficiency deteriorates as models grow deeper. Since…
FRAME: Forensic Routing and Adaptive Multi-path Evidence Fusion for Image Manipulation Detection
Kaixiang Zhao, Tianrun Yu, Aoxu Zhang +3
The proliferation of sophisticated image editing tools and generative artificial intelligence models has made verifying the authenticity of digital images increasingly challenging,…