5 citations · 6 across the 7 of their papers we have counts for
7 papers
UI-Venus-1.5 Technical Report
Venus Team, Changlong Gao, Zhangxuan Gu +24
GUI agents have emerged as a powerful paradigm for automating interactions in digital environments, yet achieving both broad generality and consistently strong task performance rem…
EEPO: Exploration-Enhanced Policy Optimization via Sample-Then-Forget
Liang Chen, Xueting Han, Qizhou Wang +4
Balancing exploration and exploitation remains a central challenge in reinforcement learning with verifiable rewards (RLVR) for large language models (LLMs). Current RLVR methods o…
Beyond Two-Stage Training: Cooperative SFT and RL for LLM Reasoning
Liang Chen, Xueting Han, Li Shen +2
Supervised fine-tuning (SFT) and reinforcement learning with verifiable rewards (RLVR) are two widely used post-training paradigms for improving the reasoning ability of large lang…
Vulnerability-Aware Alignment: Mitigating Uneven Forgetting in Harmful Fine-Tuning
Liang Chen, Xueting Han, Li Shen +2
Harmful fine-tuning (HFT), performed directly on open-source LLMs or through Fine-tuning-as-a-Service, breaks safety alignment and poses significant threats. Existing methods aim t…
PEARL: Towards Permutation-Resilient LLMs
Liang Chen, Li Shen, Yang Deng +3
The in-context learning (ICL) capability of large language models (LLMs) enables them to perform challenging tasks using provided demonstrations. However, ICL is highly sensitive t…
A Survey of the Evolution of Language Model-Based Dialogue Systems: Data, Task and Models
Hongru Wang, Lingzhi Wang, Yiming Du +4
Dialogue systems (DS), including the task-oriented dialogue system (TOD) and the open-domain dialogue system (ODD), have always been a fundamental task in natural language processi…
Self-Guard: Empower the LLM to Safeguard Itself
Zezhong Wang, Fangkai Yang, Lu Wang +5
The jailbreak attack can bypass the safety measures of a Large Language Model (LLM), generating harmful content. This misuse of LLM has led to negative societal consequences. Curre…