3 citations · 5 across the 19 of their papers we have counts for
23 papers
Think Dense, Not Long: Dynamic Decoupled Conditional Advantage for Efficient Reasoning
Keqin Peng, Yuanxin Ouyang, Xuebo Liu +4
Reinforcement Learning with Verifiable Rewards (RLVR) can elicit strong multi-step reasoning, yet it often encourages overly verbose traces. Moreover, naive length penalties in gro…
CVeDRL: An Efficient Code Verifier via Difficulty-aware Reinforcement Learning
Ji Shi, Peiming Guo, Meishan Zhang +4
Code verifiers play a critical role in post-verification for LLM-based code generation, yet existing supervised fine-tuning methods suffer from data scarcity, high failure rates, a…
Exposing the Cracks: Vulnerabilities of Retrieval-Augmented LLM-based Machine Translation
Yanming Sun, Runzhe Zhan, Chi Seng Cheang +7
\textbf{RE}trieval-\textbf{A}ugmented \textbf{L}LM-based \textbf{M}achine \textbf{T}ranslation (REAL-MT) shows promise for knowledge-intensive tasks like idiomatic translation, but…
SeaPO: Strategic Error Amplification for Robust Preference Optimization of Large Language Models
Jun Rao, Yunjie Liao, Xuebo Liu +6
Existing alignment methods for preference optimization of large language models (LLMs) aim to enhance model performance by utilizing pairs of positive and negative samples. However…
CDT: A Comprehensive Capability Framework for Large Language Models Across Cognition, Domain, and Task
Haosi Mo, Xinyu Ma, Xuebo Liu +4
Recent advances in Large Language Models (LLMs) have significantly enhanced their capabilities, highlighting the need for comprehensive evaluation frameworks that extend beyond tas…
AgentInit: Initializing LLM-based Multi-Agent Systems via Diversity and Expertise Orchestration for Effective and Efficient Collaboration
Chunhao Tian, Yutong Wang, Xuebo Liu +4
Proper initialization is crucial for any system, particularly in multi-agent systems (MAS), where it plays a pivotal role in determining both the system's efficiency and effectiven…