5 citations · 7 across the 10 of their papers we have counts for
11 papers
Knowledge-Based Zero-Replay Debugging of Multi-Agent LLM Traces
Dong Ho Kang, Hyeonjeong Cha, Daein Weon
Reliable operation of multi-agent large language model (LLM) systems depends on debugging long execution traces, where the few causally decisive events are buried in unstructured l…
One Missing Piece for Open-Source Reasoning Models: A Dataset to Mitigate Cold-Starting Short CoT LLMs in RL
Hyungjoo Chae, Dongjin Kang, Jihyuk Kim +6
With the release of R1, a publicly available large reasoning model (LRM), researchers commonly train new LRMs by training language models on R1's long chain-of-thought (CoT) infere…
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…
Coffee-Gym: An Environment for Evaluating and Improving Natural Language Feedback on Erroneous Code
Hyungjoo Chae, Taeyoon Kwon, Seungjun Moon +7
This paper presents Coffee-Gym, a comprehensive RL environment for training models that provide feedback on code editing. Coffee-Gym includes two major components: (1) Coffee, a da…
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…
Cactus: Towards Psychological Counseling Conversations using Cognitive Behavioral Theory
Suyeon Lee, Sunghwan Kim, Minju Kim +11
Recently, the demand for psychological counseling has significantly increased as more individuals express concerns about their mental health. This surge has accelerated efforts to…
Can Large Language Models be Good Emotional Supporter? Mitigating Preference Bias on Emotional Support Conversation
Dongjin Kang, Sunghwan Kim, Taeyoon Kwon +5
Emotional Support Conversation (ESC) is a task aimed at alleviating individuals' emotional distress through daily conversation. Given its inherent complexity and non-intuitive natu…
Large Language Models are Clinical Reasoners: Reasoning-Aware Diagnosis Framework with Prompt-Generated Rationales
Taeyoon Kwon, Kai Tzu-iunn Ong, Dongjin Kang +7
Machine reasoning has made great progress in recent years owing to large language models (LLMs). In the clinical domain, however, most NLP-driven projects mainly focus on clinical…
Coffee: Boost Your Code LLMs by Fixing Bugs with Feedback
Seungjun Moon, Hyungjoo Chae, Yongho Song +5
Code editing is an essential step towards reliable program synthesis to automatically correct critical errors generated from code LLMs. Recent studies have demonstrated that closed…