1 citations · 1 across the 6 of their papers we have counts for
6 papers
Identifiable Token Correspondence for World Models
Youngin Kim, Ray Sun, Inho Kim +2
Token-based transformer world models have shown strong performance in visual reinforcement learning, but often suffer from temporal inconsistency in long-horizon rollouts, includin…
Real-to-Sim Robot Policy Evaluation with Gaussian Splatting Simulation of Soft-Body Interactions
Kaifeng Zhang, Shuo Sha, Hanxiao Jiang +7
Robotic manipulation policies are advancing rapidly, but their direct evaluation in the real world remains costly, time-consuming, and difficult to reproduce, particularly for task…
Optimizing Retrieval Strategies for Financial Question Answering Documents in Retrieval-Augmented Generation Systems
Sejong Kim, Hyunseo Song, Hyunwoo Seo +1
Retrieval-Augmented Generation (RAG) has emerged as a promising framework to mitigate hallucinations in Large Language Models (LLMs), yet its overall performance is dependent on th…
Learning to Better Search with Language Models via Guided Reinforced Self-Training
Seungyong Moon, Bumsoo Park, Hyun Oh Song
While language models have shown remarkable performance across diverse tasks, they still encounter challenges in complex reasoning scenarios. Recent research suggests that language…
Compressed Context Memory For Online Language Model Interaction
Jang-Hyun Kim, Junyoung Yeom, Sangdoo Yun +1
This paper presents a context key/value compression method for Transformer language models in online scenarios, where the context continually expands. As the context lengthens, the…
Discovering Hierarchical Achievements in Reinforcement Learning via Contrastive Learning
Seungyong Moon, Junyoung Yeom, Bumsoo Park +1
Discovering achievements with a hierarchical structure in procedurally generated environments presents a significant challenge. This requires an agent to possess a broad range of a…