11 papers
Language Self-Play For Data-Free Training
Jakub Grudzien Kuba, Mengting Gu, Qi Ma +3
Large language models (LLMs) have advanced rapidly in recent years, driven by scale, abundant high-quality training data, and reinforcement learning. Yet this progress faces a fund…
Provable Scaling Laws of Feature Emergence from Learning Dynamics of Grokking
Yuandong Tian
While the phenomenon of grokking, i.e., delayed generalization, has been studied extensively, it remains an open problem whether there is a mathematical framework that characterize…
NaturalReasoning: Reasoning in the Wild with 2.8M Challenging Questions
Weizhe Yuan, Jane Yu, Song Jiang +8
Scaling reasoning capabilities beyond traditional domains such as math and coding is hindered by the lack of diverse and high-quality questions. To overcome this limitation, we int…
Training Large Language Models to Reason in a Continuous Latent Space
Shibo Hao, Sainbayar Sukhbaatar, DiJia Su +4
Large language models (LLMs) are typically constrained to reason in the language space, where they express the reasoning process through a chain-of-thought (CoT) to solve complex p…
Composing Global Solutions to Reasoning Tasks via Algebraic Objects in Neural Nets
Yuandong Tian
We prove rich algebraic structures of the solution space for 2-layer neural networks with quadratic activation and loss, trained on reasoning tasks in Abelian group (e.g., mo…
Token Assorted: Mixing Latent and Text Tokens for Improved Language Model Reasoning
DiJia Su, Hanlin Zhu, Yingchen Xu +3
Large Language Models (LLMs) excel at reasoning and planning when trained on chainof-thought (CoT) data, where the step-by-step thought process is explicitly outlined by text token…