6 papers
MEM1: Learning to Synergize Memory and Reasoning for Efficient Long-Horizon Agents
Zijian Zhou, Ao Qu, Zhaoxuan Wu +6
Modern language agents must operate over long-horizon, multi-turn interactions, where they retrieve external information, adapt to observations, and answer interdependent queries.…
LLM Meets Scene Graph: Can Large Language Models Understand and Generate Scene Graphs? A Benchmark and Empirical Study
Dongil Yang, Minjin Kim, Sunghwan Kim +5
The remarkable reasoning and generalization capabilities of Large Language Models (LLMs) have paved the way for their expanding applications in embodied AI, robotics, and other rea…
Rethinking Reward Model Evaluation Through the Lens of Reward Overoptimization
Sunghwan Kim, Dongjin Kang, Taeyoon Kwon +3
Reward models (RMs) play a crucial role in reinforcement learning from human feedback (RLHF), aligning model behavior with human preferences. However, existing benchmarks for rewar…
ToolHaystack: Stress-Testing Tool-Augmented Language Models in Realistic Long-Term Interactions
Beong-woo Kwak, Minju Kim, Dongha Lim +5
Large language models (LLMs) have demonstrated strong capabilities in using external tools to address user inquiries. However, most existing evaluations assume tool use in short co…
Web-Shepherd: Advancing PRMs for Reinforcing Web Agents
Hyungjoo Chae, Sunghwan Kim, Junhee Cho +18
Web navigation is a unique domain that can automate many repetitive real-life tasks and is challenging as it requires long-horizon sequential decision making beyond typical multimo…
Evaluating Robustness of Reward Models for Mathematical Reasoning
Sunghwan Kim, Dongjin Kang, Taeyoon Kwon +4
Reward models are key in reinforcement learning from human feedback (RLHF) systems, aligning the model behavior with human preferences. Particularly in the math domain, there have…