982 citations · 2.8k across the 47 of their papers we have counts for
49 papers
A Definition of AGI
Dan Hendrycks, Dawn Song, Christian Szegedy +30
The lack of a concrete definition for Artificial General Intelligence (AGI) obscures the gap between today's specialized AI and human-level cognition. This paper introduces a quant…
Improving Motion in Image-to-Video Models via Adaptive Low-Pass Guidance
June Suk Choi, Kyungmin Lee, Sihyun Yu +3
Recent text-to-video (T2V) models have demonstrated strong capabilities in producing high-quality, dynamic videos. To improve the visual controllability, recent works have consider…
Subtask-Aware Visual Reward Learning from Segmented Demonstrations
Changyeon Kim, Minho Heo, Doohyun Lee +4
Reinforcement Learning (RL) agents have demonstrated their potential across various robotic tasks. However, they still heavily rely on human-engineered reward functions, requiring…
Margin Matching Preference Optimization: Enhanced Model Alignment with Granular Feedback
Kyuyoung Kim, Ah Jeong Seo, Hao Liu +2
Large language models (LLMs) fine-tuned with alignment techniques, such as reinforcement learning from human feedback, have been instrumental in developing some of the most capable…
Spread Preference Annotation: Direct Preference Judgment for Efficient LLM Alignment
Dongyoung Kim, Kimin Lee, Jinwoo Shin +1
Aligning large language models (LLMs) with human preferences becomes a key component to obtaining state-of-the-art performance, but it yields a huge cost to construct a large human…
Confidence-aware Reward Optimization for Fine-tuning Text-to-Image Models
Kyuyoung Kim, Jongheon Jeong, Minyong An +4
Fine-tuning text-to-image models with reward functions trained on human feedback data has proven effective for aligning model behavior with human intent. However, excessive optimiz…
Guide Your Agent with Adaptive Multimodal Rewards
Changyeon Kim, Younggyo Seo, Hao Liu +4
Developing an agent capable of adapting to unseen environments remains a difficult challenge in imitation learning. This work presents Adaptive Return-conditioned Policy (ARP), an…
StyleDrop: Text-to-Image Generation in Any Style
Kihyuk Sohn, Nataniel Ruiz, Kimin Lee +11
Pre-trained large text-to-image models synthesize impressive images with an appropriate use of text prompts. However, ambiguities inherent in natural language and out-of-distributi…
DPOK: Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models
Ying Fan, Olivia Watkins, Yuqing Du +7
Learning from human feedback has been shown to improve text-to-image models. These techniques first learn a reward function that captures what humans care about in the task and the…
Preference Transformer: Modeling Human Preferences using Transformers for RL
Changyeon Kim, Jongjin Park, Jinwoo Shin +3
Preference-based reinforcement learning (RL) provides a framework to train agents using human preferences between two behaviors. However, preference-based RL has been challenging t…
Aligning Text-to-Image Models using Human Feedback
Kimin Lee, Hao Liu, Moonkyung Ryu +6
Deep generative models have shown impressive results in text-to-image synthesis. However, current text-to-image models often generate images that are inadequately aligned with text…
Controllability-Aware Unsupervised Skill Discovery
Seohong Park, Kimin Lee, Youngwoon Lee +1
One of the key capabilities of intelligent agents is the ability to discover useful skills without external supervision. However, the current unsupervised skill discovery methods a…
Multi-View Masked World Models for Visual Robotic Manipulation
Younggyo Seo, Junsu Kim, Stephen James +3
Visual robotic manipulation research and applications often use multiple cameras, or views, to better perceive the world. How else can we utilize the richness of multi-view data? I…
Instruction-Following Agents with Multimodal Transformer
Hao Liu, Lisa Lee, Kimin Lee +1
Humans are excellent at understanding language and vision to accomplish a wide range of tasks. In contrast, creating general instruction-following embodied agents remains a difficu…
HARP: Autoregressive Latent Video Prediction with High-Fidelity Image Generator
Younggyo Seo, Kimin Lee, Fangchen Liu +2
Video prediction is an important yet challenging problem; burdened with the tasks of generating future frames and learning environment dynamics. Recently, autoregressive latent vid…
Masked World Models for Visual Control
Younggyo Seo, Danijar Hafner, Hao Liu +4
Visual model-based reinforcement learning (RL) has the potential to enable sample-efficient robot learning from visual observations. Yet the current approaches typically train a si…
Reward Uncertainty for Exploration in Preference-based Reinforcement Learning
Xinran Liang, Katherine Shu, Kimin Lee +1
Conveying complex objectives to reinforcement learning (RL) agents often requires meticulous reward engineering. Preference-based RL methods are able to learn a more flexible rewar…
SURF: Semi-supervised Reward Learning with Data Augmentation for Feedback-efficient Preference-based Reinforcement Learning
Jongjin Park, Younggyo Seo, Jinwoo Shin +3
Preference-based reinforcement learning (RL) has shown potential for teaching agents to perform the target tasks without a costly, pre-defined reward function by learning the rewar…
Reinforcement Learning with Action-Free Pre-Training from Videos
Younggyo Seo, Kimin Lee, Stephen James +1
Recent unsupervised pre-training methods have shown to be effective on language and vision domains by learning useful representations for multiple downstream tasks. In this paper,…
Improving Transferability of Representations via Augmentation-Aware Self-Supervision
Hankook Lee, Kibok Lee, Kimin Lee +2
Recent unsupervised representation learning methods have shown to be effective in a range of vision tasks by learning representations invariant to data augmentations such as random…
B-Pref: Benchmarking Preference-Based Reinforcement Learning
Kimin Lee, Laura Smith, Anca Dragan +1
Reinforcement learning (RL) requires access to a reward function that incentivizes the right behavior, but these are notoriously hard to specify for complex tasks. Preference-based…
URLB: Unsupervised Reinforcement Learning Benchmark
Michael Laskin, Denis Yarats, Hao Liu +6
Deep Reinforcement Learning (RL) has emerged as a powerful paradigm to solve a range of complex yet specific control tasks. Yet training generalist agents that can quickly adapt to…
Towards More Generalizable One-shot Visual Imitation Learning
Zhao Mandi, Fangchen Liu, Kimin Lee +1
A general-purpose robot should be able to master a wide range of tasks and quickly learn a novel one by leveraging past experiences. One-shot imitation learning (OSIL) approaches t…
Skill Preferences: Learning to Extract and Execute Robotic Skills from Human Feedback
Xiaofei Wang, Kimin Lee, Kourosh Hakhamaneshi +2
A promising approach to solving challenging long-horizon tasks has been to extract behavior priors (skills) by fitting generative models to large offline datasets of demonstrations…