2 citations · 2 across the 8 of their papers we have counts for
12 papers
Aligning Large Language Models with Human Feedback: Mathematical Foundations and Algorithm Design
Siliang Zeng, Luca Viano, Chenliang Li +6
This article provides an introduction to the mathematical foundations and algorithmic frameworks used to align Large Language Models (LLMs) with human intentions, preferences, and…
Building Presentation Intelligence for High-Quality Slide Generation
Yuhang Xie, Cheng Li, Chenliang Li +6
PowerPoint presentations (PPTs) are a dominant medium for communication across diverse domains, yet high-quality PPT generation still depends heavily on labor-intensive human effor…
Benchmarking General Mobile Assistants in Challenging Real-World Scenarios
Yiqi Zhu, Feiyu Gao, Jiaxing Fan +7
Graphical user interfaces have emerged as an important environment for evaluating autonomous AI agents on multimodal interactive tasks. Existing benchmarks such as AndroidWorld and…
State2State: Environment-Derived Mid-Training for LLM Agents
Xuanyu Lei, Yiqi Zhu, Chenliang Li +6
Training LLM agents commonly relies on supervised fine-tuning from expert trajectories or online reinforcement learning over human-specified tasks with handcrafted verifiers. Thoug…
R2-Write: Reflection and Revision for Open-Ended Writing with Deep Reasoning
Wanlong Liu, Bo Zhang, Chenliang Li +4
While deep reasoning with long chain-of-thought has dramatically improved large language models in verifiable domains like mathematics, its effectiveness for open-ended tasks such…
Enhancing Pretrained Model-based Continual Representation Learning via Guided Random Projection
Ruilin Li, Heming Zou, Xiufeng Yan +4
Recent paradigms in Random Projection Layer (RPL)-based continual representation learning have demonstrated superior performance when building upon a pre-trained model (PTM). These…
Incentivizing In-depth Reasoning over Long Contexts with Process Advantage Shaping
Miao Peng, Weizhou Shen, Nuo Chen +3
Reinforcement Learning with Verifiable Rewards (RLVR) has proven effective in enhancing LLMs short-context reasoning, but its performance degrades in long-context scenarios that re…
CorpusQA: A 10 Million Token Benchmark for Corpus-Level Analysis and Reasoning
Zhiyuan Lu, Chenliang Li, Yingcheng Shi +3
While large language models now handle million-token contexts, their capacity for reasoning across entire document repositories remains largely untested. Existing benchmarks are in…
QwenLong-L1.5: Post-Training Recipe for Long-Context Reasoning and Memory Management
Weizhou Shen, Ziyi Yang, Chenliang Li +11
We introduce QwenLong-L1.5, a model that achieves superior long-context reasoning capabilities through systematic post-training innovations. The key technical breakthroughs of Qwen…
SPELL: Self-Play Reinforcement Learning for Evolving Long-Context Language Models
Ziyi Yang, Weizhou Shen, Chenliang Li +5
Progress in long-context reasoning for large language models (LLMs) has lagged behind other recent advances. This gap arises not only from the intrinsic difficulty of processing lo…
Writing-RL: Advancing Long-form Writing via Adaptive Curriculum Reinforcement Learning
Xuanyu Lei, Chenliang Li, Yuning Wu +7
Recent advances in Large Language Models(LLMs) have enabled strong performance in long-form writing, but current training paradigms remain limited: Supervised Fine-Tuning (SFT) rem…
MUSEG: Reinforcing Video Temporal Understanding via Timestamp-Aware Multi-Segment Grounding
Fuwen Luo, Shengfeng Lou, Chi Chen +9
Video temporal understanding is crucial for multimodal large language models (MLLMs) to reason over events in videos. Despite recent advances in general video understanding, curren…