4 papers
SPARKLING: Balancing Signal Preservation and Symmetry Breaking for Width-Progressive Learning
Qifan Yu, Xinyu Ma, Zhijian Zhuo +7
Progressive Learning (PL) reduces pre-training computational overhead by gradually increasing model scale. While prior work has extensively explored depth expansion, width expansio…
From Recognition to Reasoning: Advancing Multimodal Harmful Meme Detection via Chain-of-Thought Alignment
Hexiang Gu, Qifan Yu, Yuan Liu +4
As a multimodal communication medium that integrates images and text, memes often convey implicit harmful content through metaphors, satire, and humor, making harmful meme detectio…
Parallel Loop Transformer for Efficient Test-Time Computation Scaling
Bohong Wu, Mengzhao Chen, Xiang Luo +9
Large Language Models (LLMs) are powerful but often too slow and costly for real-world use during inference. Looped transformers save on parameters by reusing the same weights for…
Enhancing Auto-regressive Chain-of-Thought through Loop-Aligned Reasoning
Qifan Yu, Zhenyu He, Sijie Li +4
Chain-of-Thought (CoT) prompting has emerged as a powerful technique for enhancing language model's reasoning capabilities. However, generating long and correct CoT trajectories is…