39 citations · 107 across the 19 of their papers we have counts for
13 papers · 1 filter
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…
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…
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…
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…