7 papers · 1 filter
Hallucination in World Models is Predictable and Preventable
Nicklas Hansen, Xiaolong Wang
Modern generative world models render increasingly realistic action-controllable futures, yet they frequently hallucinate: rollouts remain visually fluent while drifting from the g…
Manifold Bandits: Bayesian Curriculum Learning over the Latent Geometry of Large Language Models
Darrien McKenzie, Nicklas Hansen, Xiaolong Wang
Reinforcement learning (RL) is a central approach for improving reasoning capabilities in large language models (LLMs), where training efficiency depends critically on how problems…
Learning Massively Multitask World Models for Continuous Control
Nicklas Hansen, Hao Su, Xiaolong Wang
General-purpose control demands agents that act across many tasks and embodiments, yet research on reinforcement learning (RL) for continuous control remains dominated by single-ta…
Multi-Stage Manipulation with Demonstration-Augmented Reward, Policy, and World Model Learning
Adrià López Escoriza, Nicklas Hansen, Stone Tao +2
Long-horizon tasks in robotic manipulation present significant challenges in reinforcement learning (RL) due to the difficulty of designing dense reward functions and effectively e…
Hierarchical World Models as Visual Whole-Body Humanoid Controllers
Nicklas Hansen, Jyothir S, Vlad Sobal +3
Whole-body control for humanoids is challenging due to the high-dimensional nature of the problem, coupled with the inherent instability of a bipedal morphology. Learning from visu…
PWM: Policy Learning with Multi-Task World Models
Ignat Georgiev, Varun Giridhar, Nicklas Hansen +1
Reinforcement Learning (RL) has made significant strides in complex tasks but struggles in multi-task settings with different embodiments. World model methods offer scalability by…