6 papers
MobileSafetyBench: Evaluating Safety of Autonomous Agents in Mobile Device Control
Juyong Lee, Dongyoon Hahm, June Suk Choi +2
Autonomous agents powered by large language models (LLMs) show promising potential in assistive tasks across various domains, including mobile device control. As these agents inter…
Learning to Contextualize Web Pages for Enhanced Decision Making by LLM Agents
Dongjun Lee, Juyong Lee, Kyuyoung Kim +4
Recent advances in large language models (LLMs) have led to a growing interest in developing LLM-based agents for automating web tasks. However, these agents often struggle with ev…
Unintended Misalignment from Agentic Fine-Tuning: Risks and Mitigation
Dongyoon Hahm, Taywon Min, Woogyeol Jin +1
Beyond simple text generation, Large Language Models (LLMs) have evolved into agentic systems capable of planning and interacting with external tools to solve complex tasks. This e…
DEAS: DEtached value learning with Action Sequence for Scalable Offline RL
Changyeon Kim, Haeone Lee, Younggyo Seo +2
Offline reinforcement learning (RL) presents an attractive paradigm for training intelligent agents without expensive online interactions. However, current approaches still struggl…
Understanding Impact of Human Feedback via Influence Functions
Taywon Min, Haeone Lee, Yongchan Kwon +1
In Reinforcement Learning from Human Feedback (RLHF), it is crucial to learn suitable reward models from human feedback to align large language models (LLMs) with human intentions.…
Benchmarking Mobile Device Control Agents across Diverse Configurations
Juyong Lee, Taywon Min, Minyong An +4
Mobile device control agents can largely enhance user interactions and productivity by automating daily tasks. However, despite growing interest in developing practical agents, the…