activity
20172026
most citedTopological Semantic Graph Memory for Image-Goal Navigation

7 citations · 7 across the 5 of their papers we have counts for

collaborators

7 papers

cs.AI2026

AgentHabit: Characterizing Distinct Behaviors of Agents on Everyday Tasks

Woojung Song, Hoyeol Yang, Jeonghoon Shim +4

Large language model (LLM) agents assist users with everyday tasks that can be completed in many reasonable ways. Even when their answers are useful, how agents carry out these tas…

cs.LG2026

Your Language Model is Its Own Critic: Reinforcement Learning with Value Estimation from Actor's Internal States

Yunho Choi, Jongwon Lim, Woojin Ahn +3

Reinforcement learning with verifiable rewards (RLVR) for Large Reasoning Models rests on variance reduction, which requires both a reliable baseline and high prompt diversity with…

cs.LG2026

KL for a KL: On-Policy Distillation with Control Variate Baseline

Minjae Oh, Sangjun Song, Gyubin Choi +2

On-Policy Distillation (OPD) has emerged as a dominant post-training paradigm for large language models, especially for reasoning domains. However, OPD remains unstable in practice…

cs.RO2026

Shallow-π: Knowledge Distillation for Flow-based VLAs

Boseong Jeon, Yunho Choi, Taehan Kim

The growing demand for real-time robotic deployment necessitates fast and on-device inference for vision-language-action (VLA) models. Within the VLA literature, efficiency has bee…

cs.CL2025

Future Policy Approximation for Offline Reinforcement Learning in LLM Reasoning

Minjae Oh, Yunho Choi, Dongmin Choi +1

Reinforcement learning (RL) has emerged as a key driver of post-training for complex reasoning in large language models (LLMs), yet online RL introduces substantial instability and…

cs.RO2022★ 7 cited

Topological Semantic Graph Memory for Image-Goal Navigation

Nuri Kim, Obin Kwon, Hwiyeon Yoo +3

A novel framework is proposed to incrementally collect landmark-based graph memory and use the collected memory for image goal navigation. Given a target image to search, an embodi…

cs.LG2017

Text2Action: Generative Adversarial Synthesis from Language to Action

Hyemin Ahn, Timothy Ha, Yunho Choi +2

In this paper, we propose a generative model which learns the relationship between language and human action in order to generate a human action sequence given a sentence describin…