180 citations · 454 across the 68 of their papers we have counts for
70 papers
AWM: Answerable Working Memory for Long-Document VQA Agents
Dongzhuoran Zhou, Yuqicheng Zhu, Yule Liu +5
Long-document visual question answering increasingly relies on VLM agents that retrieve candidate pages, inspect page images, write findings to working memory, and synthesize answe…
SCALECUA: Scaling Computer Use Agents with Verifiable Task Synthesis and Efficient Online RL
Bowen Lv, Xiao Liu, Yanyu Ren +7
Computer use agents (CUAs) are emerging as a powerful interface for automating complex digital workflows through visual perception and GUI execution. Online reinforcement learning…
CompactionRL: Reinforcement Learning with Context Compaction for Long-Horizon Agents
Yujiang Li, Zhenyu Hou, Yi Jing +2
Long-horizon agentic LLMs are increasingly limited by finite context windows, as extended interaction trajectories can exceed the maximum context length before a task is completed.…
MobileRL: Online Agentic Reinforcement Learning for Mobile GUI Agents
Yifan Xu, Xiao Liu, Xinghan Liu +7
Building general-purpose graphical user interface (GUI) agents has become increasingly promising with the progress in vision language models. However, developing effective mobile G…
DeepDive: Advancing Deep Search Agents with Knowledge Graphs and Multi-Turn RL
Rui Lu, Zhenyu Hou, Zihan Wang +6
Augmenting large language models (LLMs) with browsing tools substantially improves their potential as deep search agents to solve complex, real-world tasks. Yet, open LLMs still pe…
AgentRL: Scaling Agentic Reinforcement Learning with a Multi-Turn, Multi-Task Framework
Hanchen Zhang, Xiao Liu, Bowen Lv +11
Recent advances in large language models (LLMs) have sparked growing interest in building generalist agents that can learn through online interactions. However, applying reinforcem…
ReST-RL: Reinforcing LLM Reasoning through Unified Self-Training and Value-Guided Search
Sining Zhoubian, Dan Zhang, Jie Tang
With respect to improving the reasoning accuracy of LLMs, the representative reinforcement learning (RL) method - Group Relative Policy Optimization (GRPO) - has achieved critical…
ComputerRL: Scaling End-to-End Online Reinforcement Learning for Computer Use Agents
Hanyu Lai, Xiao Liu, Yanxiao Zhao +7
We introduce ComputerRL, a framework for autonomous desktop intelligence that enables agents to operate complex digital workspaces skillfully. ComputerRL features the API-GUI parad…
InvestAlign: Overcoming Data Scarcity in Aligning Large Language Models with Investor Decision-Making Processes under Herd Behavior
Huisheng Wang, Zhuoshi Pan, Hangjing Zhang +3
Aligning Large Language Models (LLMs) with investor decision-making processes under herd behavior is a critical challenge in behavioral finance, which grapples with a fundamental l…
GLM-4.5V and GLM-4.1V-Thinking: Towards Versatile Multimodal Reasoning with Scalable Reinforcement Learning
V Team, Wenyi Hong, Wenmeng Yu +90
We present GLM-4.1V-Thinking, GLM-4.5V, and GLM-4.6V, a family of vision-language models (VLMs) designed to advance general-purpose multimodal understanding and reasoning. In this…
SWE-Dev: Building Software Engineering Agents with Training and Inference Scaling
Haoran Wang, Zhenyu Hou, Yao Wei +2
Large language models (LLMs) have advanced rapidly from conversational problem solving to addressing real-world tasks involving tool use, such as software engineering (SWE). Recent…
TreeRL: LLM Reinforcement Learning with On-Policy Tree Search
Zhenyu Hou, Ziniu Hu, Yujiang Li +3
Reinforcement learning (RL) with tree search has demonstrated superior performance in traditional reasoning tasks. Compared to conventional independent chain sampling strategies wi…
AndroidGen: Building an Android Language Agent under Data Scarcity
Hanyu Lai, Junjie Gao, Xiao Liu +4
Large language models have opened up a world of possibilities for various NLP tasks, sparking optimism for the future. Despite their potential, LLMs have yet to be widely used as a…
VPO: Aligning Text-to-Video Generation Models with Prompt Optimization
Jiale Cheng, Ruiliang Lyu, Xiaotao Gu +9
Video generation models have achieved remarkable progress in text-to-video tasks. These models are typically trained on text-video pairs with highly detailed and carefully crafted…
LongSafety: Evaluating Long-Context Safety of Large Language Models
Yida Lu, Jiale Cheng, Zhexin Zhang +7
As Large Language Models (LLMs) continue to advance in understanding and generating long sequences, new safety concerns have been introduced through the long context. However, the…
Parameter-Efficient Fine-Tuning for Foundation Models
Dan Zhang, Tao Feng, Lilong Xue +3
This survey delves into the realm of Parameter-Efficient Fine-Tuning (PEFT) within the context of Foundation Models (FMs). PEFT, a cost-effective fine-tuning technique, minimizes p…
LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks
Yushi Bai, Shangqing Tu, Jiajie Zhang +9
This paper introduces LongBench v2, a benchmark designed to assess the ability of LLMs to handle long-context problems requiring deep understanding and reasoning across real-world…
MotionBench: Benchmarking and Improving Fine-grained Video Motion Understanding for Vision Language Models
Wenyi Hong, Yean Cheng, Zhuoyi Yang +6
In recent years, vision language models (VLMs) have made significant advancements in video understanding. However, a crucial capability - fine-grained motion comprehension - remain…
Does RLHF Scale? Exploring the Impacts From Data, Model, and Method
Zhenyu Hou, Pengfan Du, Yilin Niu +7
This study explores the scaling properties of Reinforcement Learning from Human Feedback (RLHF) in Large Language Models (LLMs). Although RLHF is considered an important step in po…
GLM-4-Voice: Towards Intelligent and Human-Like End-to-End Spoken Chatbot
Aohan Zeng, Zhengxiao Du, Mingdao Liu +5
We introduce GLM-4-Voice, an intelligent and human-like end-to-end spoken chatbot. It supports both Chinese and English, engages in real-time voice conversations, and varies vocal…
Scaling Speech-Text Pre-training with Synthetic Interleaved Data
Aohan Zeng, Zhengxiao Du, Mingdao Liu +4
Speech language models (SpeechLMs) accept speech input and produce speech output, allowing for more natural human-computer interaction compared to text-based large language models…
VisionReward: Fine-Grained Multi-Dimensional Human Preference Learning for Image and Video Generation
Jiazheng Xu, Yu Huang, Jiale Cheng +19
Visual generative models have achieved remarkable progress in synthesizing photorealistic images and videos, yet aligning their outputs with human preferences across critical dimen…
SPaR: Self-Play with Tree-Search Refinement to Improve Instruction-Following in Large Language Models
Jiale Cheng, Xiao Liu, Cunxiang Wang +7
Instruction-following is a fundamental capability of language models, requiring the model to recognize even the most subtle requirements in the instructions and accurately reflect…
AndroidLab: Training and Systematic Benchmarking of Android Autonomous Agents
Yifan Xu, Xiao Liu, Xueqiao Sun +7
Autonomous agents have become increasingly important for interacting with the real world. Android agents, in particular, have been recently a frequently-mentioned interaction metho…