9 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…
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…
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…
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…
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"…