5 citations · 20 across the 21 of their papers we have counts for
17 papers · 1 filter
Learning Simple Test-Time Environments for LLM Web Agents
Junxuan Li, Zijun Liu, Ziyi Huang +4
Large language model (LLM) agents have demonstrated remarkable proficiency in manually constructed environments, yet their performance frequently collapses when transitioned to com…
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…
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…
QwenLong-CPRS: Towards -LLMs with Dynamic Context Optimization
Weizhou Shen, Chenliang Li, Fanqi Wan +12
This technical report presents QwenLong-CPRS, a context compression framework designed for explicit long-context optimization, addressing prohibitive computation overhead during th…
QwenLong-L1: Towards Long-Context Large Reasoning Models with Reinforcement Learning
Fanqi Wan, Weizhou Shen, Shengyi Liao +7
Recent large reasoning models (LRMs) have demonstrated strong reasoning capabilities through reinforcement learning (RL). These improvements have primarily been observed within the…
Scaling External Knowledge Input Beyond Context Windows of LLMs via Multi-Agent Collaboration
Zijun Liu, Zhennan Wan, Peng Li +3
With the rapid advancement of post-training techniques for reasoning and information seeking, large language models (LLMs) can incorporate a large quantity of retrieved knowledge t…
AdaMMS: Model Merging for Heterogeneous Multimodal Large Language Models with Unsupervised Coefficient Optimization
Yiyang Du, Xiaochen Wang, Chi Chen +9
Recently, model merging methods have demonstrated powerful strengths in combining abilities on various tasks from multiple Large Language Models (LLMs). While previous model mergin…
Mobile-Agent-V: A Video-Guided Approach for Effortless and Efficient Operational Knowledge Injection in Mobile Automation
Junyang Wang, Haiyang Xu, Xi Zhang +4
The exponential rise in mobile device usage necessitates streamlined automation for effective task management, yet many AI frameworks fall short due to inadequate operational exper…
Advancing Language Multi-Agent Learning with Credit Re-Assignment for Interactive Environment Generalization
Zhitao He, Zijun Liu, Peng Li +5
LLM-based agents have made significant advancements in interactive environments, such as mobile operations and web browsing, and other domains beyond computer using. Current multi-…
Mobile-Agent-v2: Mobile Device Operation Assistant with Effective Navigation via Multi-Agent Collaboration
Junyang Wang, Haiyang Xu, Haitao Jia +6
Mobile device operation tasks are increasingly becoming a popular multi-modal AI application scenario. Current Multi-modal Large Language Models (MLLMs), constrained by their train…
Modeling Comparative Logical Relation with Contrastive Learning for Text Generation
Yuhao Dan, Junfeng Tian, Jie Zhou +4
Data-to-Text Generation (D2T), a classic natural language generation problem, aims at producing fluent descriptions for structured input data, such as a table. Existing D2T works m…
PANDA: Preference Adaptation for Enhancing Domain-Specific Abilities of LLMs
An Liu, Zonghan Yang, Zhenhe Zhang +6
While Large language models (LLMs) have demonstrated considerable capabilities across various natural language tasks, they often fall short of the performance achieved by domain-sp…
Browse and Concentrate: Comprehending Multimodal Content via prior-LLM Context Fusion
Ziyue Wang, Chi Chen, Yiqi Zhu +7
With the bloom of Large Language Models (LLMs), Multimodal Large Language Models (MLLMs) that incorporate LLMs with pre-trained vision models have recently demonstrated impressive…
Enabling Weak LLMs to Judge Response Reliability via Meta Ranking
Zijun Liu, Boqun Kou, Peng Li +4
Despite the strong performance of large language models (LLMs) across a wide range of tasks, they still have reliability issues. Previous studies indicate that strong LLMs like GPT…
Knowledge Distillation of Black-Box Large Language Models
Hongzhan Chen, Ruijun Chen, Yuqi Yi +4
Given the exceptional performance of proprietary large language models (LLMs) like GPT-4, recent research has increasingly focused on boosting the capabilities of smaller models th…
AMBER: An LLM-free Multi-dimensional Benchmark for MLLMs Hallucination Evaluation
Junyang Wang, Yuhang Wang, Guohai Xu +8
Despite making significant progress in multi-modal tasks, current Multi-modal Large Language Models (MLLMs) encounter the significant challenge of hallucinations, which may lead to…
MCC-KD: Multi-CoT Consistent Knowledge Distillation
Hongzhan Chen, Siyue Wu, Xiaojun Quan +3
Large language models (LLMs) have showcased remarkable capabilities in complex reasoning through chain of thought (CoT) prompting. Recently, there has been a growing interest in tr…