activity
20222026
most citedQuality-Diversity through AI Feedback

7 citations · 25 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CV2026

Cosmos 3: Omnimodal World Models for Physical AI

NVIDIA, :, Aditi +293

We introduce Cosmos 3, a family of omnimodal world models designed to jointly process and generate language, image, video, audio, and action sequences within a unified mixture-of-t…

cs.AI2025★ 6 cited

Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents

Jenny Zhang, Shengran Hu, Cong Lu +2

Today's AI systems have human-designed, fixed architectures and cannot autonomously and continuously improve themselves. The advance of AI could itself be automated. If done safely…

cs.AI2024★ 3 cited

OMNI-EPIC: Open-endedness via Models of human Notions of Interestingness with Environments Programmed in Code

Maxence Faldor, Jenny Zhang, Antoine Cully +1

Open-ended and AI-generating algorithms aim to continuously generate and solve increasingly complex tasks indefinitely, offering a promising path toward more general intelligence.…

cs.CL2023★ 7 cited

Quality-Diversity through AI Feedback

Herbie Bradley, Andrew Dai, Hannah Teufel +7

In many text-generation problems, users may prefer not only a single response, but a diverse range of high-quality outputs from which to choose. Quality-diversity (QD) search algor…

cs.AI2023★ 5 cited

Quality Diversity through Human Feedback: Towards Open-Ended Diversity-Driven Optimization

Li Ding, Jenny Zhang, Jeff Clune +2

Reinforcement Learning from Human Feedback (RLHF) has shown potential in qualitative tasks where easily defined performance measures are lacking. However, there are drawbacks when…

cs.AI2023★ 4 cited

OMNI: Open-endedness via Models of human Notions of Interestingness

Jenny Zhang, Joel Lehman, Kenneth Stanley +1

Open-ended algorithms aim to learn new, interesting behaviors forever. That requires a vast environment search space, but there are thus infinitely many possible tasks. Even after…

cs.RO2023

Perceptive Locomotion with Controllable Pace and Natural Gait Transitions Over Uneven Terrains

Daniel Chee Hian Tan, Jenny Zhang, Michael +2

This work developed a learning framework for perceptive legged locomotion that combines visual feedback, proprioceptive information, and active gait regulation of foot-ground conta…

cs.LG2022

Good Time to Ask: A Learning Framework for Asking for Help in Embodied Visual Navigation

Jenny Zhang, Samson Yu, Jiafei Duan +1

In reality, it is often more efficient to ask for help than to search the entire space to find an object with an unknown location. We present a learning framework that enables an a…