40 citations · 145 across the 25 of their papers we have counts for
26 papers
RoboWorld: Fast and Reliable Neural Simulators for Generalist Robot Policy Evaluation
Byeongguk Jeon, Seonghyeon Ye, JaeHyeok Doo +4
Video world models are emerging as a scalable alternative for evaluating generalist robot policies, bypassing the physical constraints and engineering burdens of real-world deploym…
Q-Flow: Stable and Expressive Reinforcement Learning with Flow-Based Policy
JaeHyeok Doo, Byeongguk Jeon, Seonghyeon Ye +2
There is growing interest in utilizing flow-based models as decision-making policies in reinforcement learning due to their high expressive capacity. However, effectively leveragin…
World Action Models are Zero-shot Policies
Seonghyeon Ye, Yunhao Ge, Kaiyuan Zheng +33
State-of-the-art Vision-Language-Action (VLA) models excel at semantic generalization but struggle to generalize to unseen physical motions in novel environments. We introduce Drea…
DreamDojo: A Generalist Robot World Model from Large-Scale Human Videos
Shenyuan Gao, William Liang, Kaiyuan Zheng +27
Being able to simulate the outcomes of actions in varied environments will revolutionize the development of generalist agents at scale. However, modeling these world dynamics, espe…
World Simulation with Video Foundation Models for Physical AI
NVIDIA, :, Arslan Ali +87
We introduce [Cosmos-Predict2.5], the latest generation of the Cosmos World Foundation Models for Physical AI. Built on a flow-based architecture, [Cosmos-Predict2.5] unifies Text2…
DreamGen: Unlocking Generalization in Robot Learning through Video World Models
Joel Jang, Seonghyeon Ye, Zongyu Lin +25
We introduce DreamGen, a simple yet highly effective 4-stage pipeline for training robot policies that generalize across behaviors and environments through neural trajectories - sy…
FLARE: Robot Learning with Implicit World Modeling
Ruijie Zheng, Jing Wang, Scott Reed +18
We introduce uture tent presentation Alignment (), a novel framework that integrates predictive latent world modeling into rob…
GR00T N1: An Open Foundation Model for Generalist Humanoid Robots
NVIDIA, :, Johan Bjorck +40
General-purpose robots need a versatile body and an intelligent mind. Recent advancements in humanoid robots have shown great promise as a hardware platform for building generalist…
Magma: A Foundation Model for Multimodal AI Agents
Jianwei Yang, Reuben Tan, Qianhui Wu +10
We present Magma, a foundation model that serves multimodal AI agentic tasks in both the digital and physical worlds. Magma is a significant extension of vision-language (VL) model…
Latent Action Pretraining from Videos
Seonghyeon Ye, Joel Jang, Byeongguk Jeon +13
We introduce Latent Action Pretraining for general Action models (LAPA), an unsupervised method for pretraining Vision-Language-Action (VLA) models without ground-truth robot actio…
How Do Large Language Models Acquire Factual Knowledge During Pretraining?
Hoyeon Chang, Jinho Park, Seonghyeon Ye +4
Despite the recent observation that large language models (LLMs) can store substantial factual knowledge, there is a limited understanding of the mechanisms of how they acquire fac…
The BiGGen Bench: A Principled Benchmark for Fine-grained Evaluation of Language Models with Language Models
Seungone Kim, Juyoung Suk, Ji Yong Cho +29
As language models (LMs) become capable of handling a wide range of tasks, their evaluation is becoming as challenging as their development. Most generation benchmarks currently as…
Self-Explore: Enhancing Mathematical Reasoning in Language Models with Fine-grained Rewards
Hyeonbin Hwang, Doyoung Kim, Seungone Kim +2
Training on large amounts of rationales (i.e., CoT Fine-tuning) is effective at improving the reasoning capabilities of large language models (LLMs). However, acquiring human-autho…
INSTRUCTIR: A Benchmark for Instruction Following of Information Retrieval Models
Hanseok Oh, Hyunji Lee, Seonghyeon Ye +4
Despite the critical need to align search targets with users' intention, retrievers often only prioritize query information without delving into the users' intended search context.…
FLASK: Fine-grained Language Model Evaluation based on Alignment Skill Sets
Seonghyeon Ye, Doyoung Kim, Sungdong Kim +6
Evaluation of Large Language Models (LLMs) is challenging because instruction-following necessitates alignment with human values and the required set of skills varies depending on…
Improving Probability-based Prompt Selection Through Unified Evaluation and Analysis
Sohee Yang, Jonghyeon Kim, Joel Jang +3
Previous works in prompt engineering for large language models have introduced different gradient-free probability-based prompt selection methods that aim to choose the optimal pro…
The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning
Seungone Kim, Se June Joo, Doyoung Kim +4
Language models (LMs) with less than 100B parameters are known to perform poorly on chain-of-thought (CoT) reasoning in contrast to large LMs when solving unseen tasks. In this wor…
Exploring the Benefits of Training Expert Language Models over Instruction Tuning
Joel Jang, Seungone Kim, Seonghyeon Ye +5
Recently, Language Models (LMs) instruction-tuned on multiple tasks, also known as multitask-prompted fine-tuning (MT), have shown the capability to generalize to unseen tasks. Pre…
Investigating the Effectiveness of Task-Agnostic Prefix Prompt for Instruction Following
Seonghyeon Ye, Hyeonbin Hwang, Sohee Yang +3
In this paper, we present our finding that prepending a Task-Agnostic Prefix Prompt (TAPP) to the input improves the instruction-following ability of various Large Language Models…
Efficiently Enhancing Zero-Shot Performance of Instruction Following Model via Retrieval of Soft Prompt
Seonghyeon Ye, Joel Jang, Doyoung Kim +2
Enhancing the zero-shot performance of instruction-following models requires heavy computation, either by scaling the total number of training datasets or the model size. In this w…
Guess the Instruction! Flipped Learning Makes Language Models Stronger Zero-Shot Learners
Seonghyeon Ye, Doyoung Kim, Joel Jang +2
Meta-training, which fine-tunes the language model (LM) on various downstream tasks by maximizing the likelihood of the target label given the task instruction and input instance,…
Can Large Language Models Truly Understand Prompts? A Case Study with Negated Prompts
Joel Jang, Seonghyeon Ye, Minjoon Seo
Previous work has shown that there exists a scaling law between the size of Language Models (LMs) and their zero-shot performance on different downstream NLP tasks. In this work, w…
TemporalWiki: A Lifelong Benchmark for Training and Evaluating Ever-Evolving Language Models
Joel Jang, Seonghyeon Ye, Changho Lee +5
Language Models (LMs) become outdated as the world changes; they often fail to perform tasks requiring recent factual information which was absent or different during training, a p…
Efficient Contrastive Learning via Novel Data Augmentation and Curriculum Learning
Seonghyeon Ye, Jiseon Kim, Alice Oh
We introduce EfficientCL, a memory-efficient continual pretraining method that applies contrastive learning with novel data augmentation and curriculum learning. For data augmentat…