When To Solve, When To Verify: Compute-Optimal Problem Solving and Generative Verification for LLM Reasoning Paper • 2504.01005 • Published about 19 hours ago • 6
OpenVLThinker: An Early Exploration to Complex Vision-Language Reasoning via Iterative Self-Improvement Paper • 2503.17352 • Published 12 days ago • 21
Project Alexandria: Towards Freeing Scientific Knowledge from Copyright Burdens via LLMs Paper • 2502.19413 • Published Feb 26 • 19
Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language Models Paper • 2502.17387 • Published Feb 24 • 5
Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Though Paper • 2501.04682 • Published Jan 8 • 95
DataComp: In search of the next generation of multimodal datasets Paper • 2304.14108 • Published Apr 27, 2023 • 2
Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias Paper • 2306.15895 • Published Jun 28, 2023
SugarCrepe: Fixing Hackable Benchmarks for Vision-Language Compositionality Paper • 2306.14610 • Published Jun 26, 2023
Subclass-balancing Contrastive Learning for Long-tailed Recognition Paper • 2306.15925 • Published Jun 28, 2023
AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework Paper • 2308.08155 • Published Aug 16, 2023 • 7
When to Learn What: Model-Adaptive Data Augmentation Curriculum Paper • 2309.04747 • Published Sep 9, 2023
Training Language Model Agents without Modifying Language Models Paper • 2402.11359 • Published Feb 17, 2024 • 2
m&m's: A Benchmark to Evaluate Tool-Use for multi-step multi-modal Tasks Paper • 2403.11085 • Published Mar 17, 2024
DataComp-LM: In search of the next generation of training sets for language models Paper • 2406.11794 • Published Jun 17, 2024 • 52