6 papers
Reliable and Responsible Foundation Models: A Comprehensive Survey
Xinyu Yang, Junlin Han, Rishi Bommasani +49
Foundation models, including Large Language Models (LLMs), Multimodal Large Language Models (MLLMs), Image Generative Models (i.e, Text-to-Image Models and Image-Editing Models), a…
Just Enough Thinking: Efficient Reasoning with Adaptive Length Penalties Reinforcement Learning
Violet Xiang, Chase Blagden, Rafael Rafailov +4
Large reasoning models (LRMs) achieve higher performance on challenging reasoning tasks by generating more tokens at inference time, but this verbosity often wastes computation on…
Open X-Embodiment: Robotic Learning Datasets and RT-X Models
Embodiment Collaboration, Abby O'Neill, Abdul Rehman +291
Large, high-capacity models trained on diverse datasets have shown remarkable successes on efficiently tackling downstream applications. In domains from NLP to Computer Vision, thi…
Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language Models
Alon Albalak, Duy Phung, Nathan Lile +8
Increasing interest in reasoning models has led math to become a prominent testing ground for algorithmic and methodological improvements. However, existing open math datasets eith…
MJ-VIDEO: Fine-Grained Benchmarking and Rewarding Video Preferences in Video Generation
Haibo Tong, Zhaoyang Wang, Zhaorun Chen +11
Recent advancements in video generation have significantly improved the ability to synthesize videos from text instructions. However, existing models still struggle with key challe…
Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thought
Violet Xiang, Charlie Snell, Kanishk Gandhi +11
We propose a novel framework, Meta Chain-of-Thought (Meta-CoT), which extends traditional Chain-of-Thought (CoT) by explicitly modeling the underlying reasoning required to arrive…