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20232026
most citedData Pruning via Moving-one-Sample-out

6 citations · 6 across the 13 of their papers we have counts for

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5 papers · 1 filter

cs.CL2025

SmartSwitch: Advancing LLM Reasoning by Overcoming Underthinking via Promoting Deeper Thought Exploration

Xichen Zhang, Sitong Wu, Haoru Tan +4

The long chain-of-thought (LongCoT) capability is central to the recent breakthroughs achieved by large language models in complex reasoning tasks. However, the accompanying issue…

cs.CV2025

DreamOmni2: Multimodal Instruction-based Editing and Generation

Bin Xia, Bohao Peng, Yuechen Zhang +10

Recent advancements in instruction-based image editing and subject-driven generation have garnered significant attention, yet both tasks still face limitations in meeting practical…

cs.CL2025

Scaf-GRPO: Scaffolded Group Relative Policy Optimization for Enhancing LLM Reasoning

Xichen Zhang, Sitong Wu, Yinghao Zhu +4

Reinforcement learning from verifiable rewards has emerged as a powerful technique for enhancing the complex reasoning abilities of Large Language Models (LLMs). However, these met…

cs.LG2025

Understanding Data Influence with Differential Approximation

Haoru Tan, Sitong Wu, Xiuzhe Wu +5

Data plays a pivotal role in the groundbreaking advancements in artificial intelligence. The quantitative analysis of data significantly contributes to model training, enhancing bo…

cs.CV2025

Data Pruning by Information Maximization

Haoru Tan, Sitong Wu, Wei Huang +2

In this paper, we present InfoMax, a novel data pruning method, also known as coreset selection, designed to maximize the information content of selected samples while minimizing r…