39 citations · 107 across the 19 of their papers we have counts for
19 papers
Policy and World Modeling Co-Training for Language Agents
Ning Lu, Baijiong Lin, Shengcai Liu +9
Reinforcement learning (RL) improves large language model (LLM) agents by teaching them which actions lead to high rewards, but provides little supervision on what those actions do…
RAIN-Merging: A Gradient-Free Method to Enhance Instruction Following in Large Reasoning Models with Preserved Thinking Format
Zhehao Huang, Yuhang Liu, Baijiong Lin +5
Large reasoning models (LRMs) excel at a long chain of reasoning but often fail to faithfully follow instructions regarding output format, constraints, or specific requirements. We…
VL-RouterBench: A Benchmark for Vision-Language Model Routing
Zhehao Huang, Baijiong Lin, Jingyuan Zhang +5
Multi-model routing has evolved from an engineering technique into essential infrastructure, yet existing work lacks a systematic, reproducible benchmark for evaluating vision-lang…
V-ReasonBench: Toward Unified Reasoning Benchmark Suite for Video Generation Models
Yang Luo, Xuanlei Zhao, Baijiong Lin +7
Recent progress in generative video models, such as Veo-3, has shown surprising zero-shot reasoning abilities, creating a growing need for systematic and reliable evaluation. We in…
Efficient Reasoning for Large Reasoning Language Models via Certainty-Guided Reflection Suppression
Jiameng Huang, Baijiong Lin, Guhao Feng +3
Recent Large Reasoning Language Models (LRLMs) employ long chain-of-thought reasoning with complex reflection behaviors, typically signaled by specific trigger words (e.g., "Wait"…
PARM: Multi-Objective Test-Time Alignment via Preference-Aware Autoregressive Reward Model
Baijiong Lin, Weisen Jiang, Yuancheng Xu +2
Multi-objective test-time alignment aims to adapt large language models (LLMs) to diverse multi-dimensional user preferences during inference while keeping LLMs frozen. Recently, G…
Gradient-Based Multi-Objective Deep Learning: Algorithms, Theories, Applications, and Beyond
Weiyu Chen, Baijiong Lin, Xiaoyuan Zhang +4
Many modern deep learning applications require balancing multiple objectives that are often conflicting. Examples include multi-task learning, fairness-aware learning, and the alig…
RouterDC: Query-Based Router by Dual Contrastive Learning for Assembling Large Language Models
Shuhao Chen, Weisen Jiang, Baijiong Lin +2
Recent works show that assembling multiple off-the-shelf large language models (LLMs) can harness their complementary abilities. To achieve this, routing is a promising method, whi…
MTMamba++: Enhancing Multi-Task Dense Scene Understanding via Mamba-Based Decoders
Baijiong Lin, Weisen Jiang, Pengguang Chen +2
Multi-task dense scene understanding, which trains a model for multiple dense prediction tasks, has a wide range of application scenarios. Capturing long-range dependency and enhan…
MTMamba: Enhancing Multi-Task Dense Scene Understanding by Mamba-Based Decoders
Baijiong Lin, Weisen Jiang, Pengguang Chen +3
Multi-task dense scene understanding, which learns a model for multiple dense prediction tasks, has a wide range of application scenarios. Modeling long-range dependency and enhanc…
A First-Order Multi-Gradient Algorithm for Multi-Objective Bi-Level Optimization
Feiyang Ye, Baijiong Lin, Xiaofeng Cao +2
In this paper, we study the Multi-Objective Bi-Level Optimization (MOBLO) problem, where the upper-level subproblem is a multi-objective optimization problem and the lower-level su…
BYOM: Building Your Own Multi-Task Model For Free
Weisen Jiang, Baijiong Lin, Han Shi +3
Recently, various merging methods have been proposed to build a multi-task model from task-specific finetuned models without retraining. However, existing methods suffer from a lar…
Efficient Transfer Learning in Diffusion Models via Adversarial Noise
Xiyu Wang, Baijiong Lin, Daochang Liu +1
Diffusion Probabilistic Models (DPMs) have demonstrated substantial promise in image generation tasks but heavily rely on the availability of large amounts of training data. Previo…
Dual-Balancing for Multi-Task Learning
Baijiong Lin, Weisen Jiang, Feiyang Ye +6
Multi-task learning aims to learn multiple related tasks simultaneously and has achieved great success in various fields. However, the disparity in loss and gradient scales among t…
LibMTL: A Python Library for Multi-Task Learning
Baijiong Lin, Yu Zhang
This paper presents LibMTL, an open-source Python library built on PyTorch, which provides a unified, comprehensive, reproducible, and extensible implementation framework for Multi…
Reasonable Effectiveness of Random Weighting: A Litmus Test for Multi-Task Learning
Baijiong Lin, Feiyang Ye, Yu Zhang +1
Multi-Task Learning (MTL) has achieved success in various fields. However, how to balance different tasks to achieve good performance is a key problem. To achieve the task balancin…
Multi-Objective Meta Learning
Feiyang Ye, Baijiong Lin, Zhixiong Yue +3
Meta learning with multiple objectives can be formulated as a Multi-Objective Bi-Level optimization Problem (MOBLP) where the upper-level subproblem is to solve several possible co…
Effective, Efficient and Robust Neural Architecture Search
Zhixiong Yue, Baijiong Lin, Xiaonan Huang +1
Recent advances in adversarial attacks show the vulnerability of deep neural networks searched by Neural Architecture Search (NAS). Although NAS methods can find network architectu…
Binary Neutron Stars Gravitational Wave Detection Based on Wavelet Packet Analysis And Convolutional Neural Networks
Baijiong Lin, Xiangru Li, Woliang Yu
This work investigates the detection of binary neutron stars gravitational wave based on convolutional neural network (CNN). To promote the detection performance and efficiency, we…