14 citations · 14 across the 7 of their papers we have counts for
8 papers
StructRL: Online Structured Reinforcement Learning for Long-Horizon Vision-Language-Action Tasks
Ziyi Yin, Sangmin Woo, Kang Zhou +4
Vision-language-action (VLA) models perform well on shorter-horizon manipulation tasks but still struggle with long-horizon tasks that require multiple dependent manipulations from…
An Empirical Study of Automating Agent Evaluation
Kang Zhou, Sangmin Woo, Haibo Ding +15
Agent evaluation requires assessing complex multi-step behaviors involving tool use and intermediate reasoning, making it costly and expertise-intensive. A natural question arises:…
Diffusion Language Model Inference with Monte Carlo Tree Search
Zheng Huang, Kiran Ramnath, Yueyan Chen +8
Diffusion language models (DLMs) have recently emerged as a compelling alternative to autoregressive generation, offering parallel generation and improved global coherence. During…
Learning to Ideate for Machine Learning Engineering Agents
Yunxiang Zhang, Kang Zhou, Zhichao Xu +5
Existing machine learning engineering (MLE) agents struggle to iteratively optimize their implemented algorithms for effectiveness. To address this, we introduce MLE-Ideator, a dua…
Beyond Correctness: Rewarding Faithful Reasoning in Retrieval-Augmented Generation
Zhichao Xu, Zongyu Wu, Yun Zhou +9
Inspired by the success of reinforcement learning (RL) in Large Language Model (LLM) training for domains like math and code, recent work has begun training LLMs to dynamically pla…
Think, Verbalize, then Speak: Bridging Complex Thoughts and Comprehensible Speech
Sang Hoon Woo, Sehun Lee, Kang-wook Kim +1
Spoken dialogue systems increasingly employ large language models (LLMs) to leverage their advanced reasoning capabilities. However, direct application of LLMs in spoken communicat…
Black-Box Visual Prompt Engineering for Mitigating Object Hallucination in Large Vision Language Models
Sangmin Woo, Kang Zhou, Yun Zhou +4
Large Vision Language Models (LVLMs) often suffer from object hallucination, which undermines their reliability. Surprisingly, we find that simple object-based visual prompting --…
A Systematic Survey of Automatic Prompt Optimization Techniques
Kiran Ramnath, Kang Zhou, Sheng Guan +18
Since the advent of large language models (LLMs), prompt engineering has been a crucial step for eliciting desired responses for various Natural Language Processing (NLP) tasks. Ho…