1 citations · 3 across the 11 of their papers we have counts for
9 papers · 1 filter
WebSTAR: Scalable Data Synthesis for Computer Use Agents with Step-Level Filtering
Yifei He, Pranit Chawla, Yaser Souri +2
Computer use agents (CUAs) can operate real-world digital interfaces but remain difficult to train due to the high cost of graphical user interface (GUI) interaction and the scarci…
MergeBench: A Benchmark for Merging Domain-Specialized LLMs
Yifei He, Siqi Zeng, Yuzheng Hu +3
Model merging provides a scalable alternative to multi-task training by combining specialized finetuned models through parameter arithmetic, enabling efficient deployment without t…
Efficiently Editing Mixture-of-Experts Models with Compressed Experts
Yifei He, Yang Liu, Chen Liang +1
Mixture-of-Experts (MoE) models have become a key approach for scaling large language models efficiently by activating only a subset of experts during training and inference. Typic…
Task Vector Bases: A Unified and Scalable Framework for Compressed Task Arithmetic
Siqi Zeng, Yifei He, Meitong Liu +5
Task arithmetic, representing downstream tasks through linear operations on task vectors, has emerged as a simple yet powerful paradigm for transferring knowledge across diverse se…
Semi-Supervised Reward Modeling via Iterative Self-Training
Yifei He, Haoxiang Wang, Ziyan Jiang +2
Reward models (RM) capture the values and preferences of humans and play a central role in Reinforcement Learning with Human Feedback (RLHF) to align pretrained large language mode…
Localize-and-Stitch: Efficient Model Merging via Sparse Task Arithmetic
Yifei He, Yuzheng Hu, Yong Lin +2
Model merging offers an effective strategy to combine the strengths of multiple finetuned models into a unified model that preserves the specialized capabilities of each. Existing…
Robust Multi-Task Learning with Excess Risks
Yifei He, Shiji Zhou, Guojun Zhang +5
Multi-task learning (MTL) considers learning a joint model for multiple tasks by optimizing a convex combination of all task losses. To solve the optimization problem, existing met…
Gradual Domain Adaptation: Theory and Algorithms
Yifei He, Haoxiang Wang, Bo Li +1
Unsupervised domain adaptation (UDA) adapts a model from a labeled source domain to an unlabeled target domain in a one-off way. Though widely applied, UDA faces a great challenge…
Greedy Modality Selection via Approximate Submodular Maximization
Runxiang Cheng, Gargi Balasubramaniam, Yifei He +2
Multimodal learning considers learning from multi-modality data, aiming to fuse heterogeneous sources of information. However, it is not always feasible to leverage all available m…