5 citations · 6 across the 9 of their papers we have counts for
9 papers
LongAct: Harnessing Intrinsic Activation Patterns for Long-Context Reinforcement Learning
Bowen Ping, Zijun Chen, Tingfeng Hui +4
Reinforcement Learning (RL) has emerged as a critical driver for enhancing the reasoning capabilities of Large Language Models (LLMs). While recent advancements have focused on rew…
LongR: Unleashing Long-Context Reasoning via Reinforcement Learning with Dense Utility Rewards
Qize Yu, Bowen Ping, Zijun Chen +6
Reinforcement Learning (RL) has emerged as a key driver for LLM reasoning. This capability is equally pivotal in long-context scenarios---such as long-dialogue understanding and st…
GeoBench: Rethinking Multimodal Geometric Problem-Solving via Hierarchical Evaluation
Yuan Feng, Yue Yang, Xiaohan He +8
Geometric problem solving constitutes a critical branch of mathematical reasoning, requiring precise analysis of shapes and spatial relationships. Current evaluations of geometric…
SE-Merging: A Self-Enhanced Approach for Dynamic Model Merging
Zijun Chen, Zhanpeng Zhou, Bo Zhang +3
Model merging has gained increasing attention due to its intriguing property: interpolating the parameters of different task-specific fine-tuned models leads to multi-task abilitie…
ProtoReasoning: Prototypes as the Foundation for Generalizable Reasoning in LLMs
Feng He, Zijun Chen, Xinnian Liang +4
Recent advances in Large Reasoning Models (LRMs) trained with Long Chain-of-Thought (Long CoT) reasoning have demonstrated remarkable cross-domain generalization capabilities. Howe…
TrustGeoGen: Formal-Verified Data Engine for Trustworthy Multi-modal Geometric Problem Solving
Daocheng Fu, Jianlong Chen, Renqiu Xia +12
Geometric problem solving (GPS) requires precise multimodal understanding and rigorous, step-by-step logical reasoning. However, developing capable Multimodal Large Language Models…
AMP: Autoregressive Motion Prediction Revisited with Next Token Prediction for Autonomous Driving
Xiaosong Jia, Shaoshuai Shi, Zijun Chen +4
As an essential task in autonomous driving (AD), motion prediction aims to predict the future states of surround objects for navigation. One natural solution is to estimate the pos…
ChartX & ChartVLM: A Versatile Benchmark and Foundation Model for Complicated Chart Reasoning
Renqiu Xia, Bo Zhang, Hancheng Ye +9
Recently, many versatile Multi-modal Large Language Models (MLLMs) have emerged continuously. However, their capacity to query information depicted in visual charts and engage in r…
On the Emergence of Cross-Task Linearity in the Pretraining-Finetuning Paradigm
Zhanpeng Zhou, Zijun Chen, Yilan Chen +2
The pretraining-finetuning paradigm has become the prevailing trend in modern deep learning. In this work, we discover an intriguing linear phenomenon in models that are initialize…