2 citations · 3 across the 9 of their papers we have counts for
10 papers
Beyond Solvability: Task Learnability as a Static Prior for LLM RL Post-Training
Ting Zhou, Zhenqing Ling, Daoyuan Chen +4
Reinforcement learning (RL) has become a central post-training paradigm for eliciting reasoning capabilities in large language models, yet uniform task sampling allocates compute w…
GEOALIGN: Geometric Rollout Curation for Robust LLM Reinforcement Learning
Ting Zhou, Zhenqing Ling, Yiyang Zhao +2
Online reinforcement learning is widely used to align large language models (LLMs) with reward signals, yet training can be unstable under noisy or misspecified rewards. We identif…
Zero-VC: Zero-Lookahead Streaming Voice Conversion via Speaker Anonymization
Yudong Li, Zihao Fang, Junwen Qiu +4
Streaming zero-shot voice conversion struggles to disentangle timbre from linguistic content without degrading utility or inflating latency. Current methods rely on information bot…
DetailMaster: Can Your Text-to-Image Model Handle Long Prompts?
Qirui Jiao, Daoyuan Chen, Yilun Huang +3
While recent Text-to-Image (T2I) models show impressive capabilities in synthesizing images from brief descriptions, they struggle with the long, detailed prompts required for prof…
HumanVBench: Probing Human-Centric Video Understanding in MLLMs with Automatically Synthesized Benchmarks
Ting Zhou, Daoyuan Chen, Qirui Jiao +3
Evaluating the nuanced human-centric video understanding capabilities of Multimodal Large Language Models (MLLMs) remains a great challenge, as existing benchmarks often overlook t…
Img-Diff: Contrastive Data Synthesis for Multimodal Large Language Models
Qirui Jiao, Daoyuan Chen, Yilun Huang +3
High-performance Multimodal Large Language Models (MLLMs) are heavily dependent on data quality. To advance fine-grained image recognition within MLLMs, we introduce a novel data s…
ChartThinker: A Contextual Chain-of-Thought Approach to Optimized Chart Summarization
Mengsha Liu, Daoyuan Chen, Yaliang Li +2
Data visualization serves as a critical means for presenting data and mining its valuable insights. The task of chart summarization, through natural language processing techniques,…
On the Convergence of Zeroth-Order Federated Tuning for Large Language Models
Zhenqing Ling, Daoyuan Chen, Liuyi Yao +2
The confluence of Federated Learning (FL) and Large Language Models (LLMs) is ushering in a new era in privacy-preserving natural language processing. However, the intensive memory…
From Training-Free to Adaptive: Empirical Insights into MLLMs' Understanding of Detection Information
Qirui Jiao, Daoyuan Chen, Yilun Huang +2
Despite the impressive capabilities of Multimodal Large Language Models (MLLMs) in integrating text and image modalities, challenges remain in accurately interpreting detailed visu…
Tunable Soft Prompts are Messengers in Federated Learning
Chenhe Dong, Yuexiang Xie, Bolin Ding +2
Federated learning (FL) enables multiple participants to collaboratively train machine learning models using decentralized data sources, alleviating privacy concerns that arise fro…