Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Paper • 2412.02980 • Published 22 days ago • 12
Adaptive Inference-Time Compute: LLMs Can Predict if They Can Do Better, Even Mid-Generation Paper • 2410.02725 • Published Oct 3 • 1
Open X-Embodiment: Robotic Learning Datasets and RT-X Models Paper • 2310.08864 • Published Oct 13, 2023 • 2
RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control Paper • 2307.15818 • Published Jul 28, 2023 • 28
D5RL: Diverse Datasets for Data-Driven Deep Reinforcement Learning Paper • 2408.08441 • Published Aug 15 • 7
Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data Paper • 2404.14367 • Published Apr 22 • 1
LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language Models Paper • 2311.18232 • Published Nov 30, 2023 • 1
Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters Paper • 2408.03314 • Published Aug 6 • 51
MJ-Bench: Is Your Multimodal Reward Model Really a Good Judge for Text-to-Image Generation? Paper • 2407.04842 • Published Jul 5 • 52
Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning Paper • 2305.12295 • Published May 20, 2023
The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources Paper • 2406.16746 • Published Jun 24
DataComp-LM: In search of the next generation of training sets for language models Paper • 2406.11794 • Published Jun 17 • 50
Offline Regularised Reinforcement Learning for Large Language Models Alignment Paper • 2405.19107 • Published May 29 • 14