978 citations · 1k across the 14 of their papers we have counts for
9 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…
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…
Inference-Time Scaling for Generalist Reward Modeling
Zijun Liu, Peiyi Wang, Runxin Xu +5
Reinforcement learning (RL) has been widely adopted in post-training for large language models (LLMs) at scale. Recently, the incentivization of reasoning capabilities in LLMs from…
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-…
DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning
DeepSeek-AI, Daya Guo, Dejian Yang +195
General reasoning represents a long-standing and formidable challenge in artificial intelligence. Recent breakthroughs, exemplified by large language models (LLMs) and chain-of-tho…
Enhancing Multilingual Capabilities of Large Language Models through Self-Distillation from Resource-Rich Languages
Yuanchi Zhang, Yile Wang, Zijun Liu +5
While large language models (LLMs) have been pre-trained on multilingual corpora, their performance still lags behind in most languages compared to a few resource-rich languages. O…
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…
A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration
Zijun Liu, Yanzhe Zhang, Peng Li +2
Recent studies show that collaborating multiple large language model (LLM) powered agents is a promising way for task solving. However, current approaches are constrained by using…
An Extensible Plug-and-Play Method for Multi-Aspect Controllable Text Generation
Xuancheng Huang, Zijun Liu, Peng Li +3
Recently, multi-aspect controllable text generation that controls the generated text in multiple aspects (e.g., sentiment, topic, and keywords) has attracted increasing attention.…