1 citations · 2 across the 5 of their papers we have counts for
5 papers
Scaling Self-Play with Self-Guidance
Luke Bailey, Kaiyue Wen, Kefan Dong +2
LLM self-play algorithms are notable in that, in principle, nothing bounds their learning: a Conjecturer model creates problems for a Solver, and both improve together. However, in…
STP: Self-play LLM Theorem Provers with Iterative Conjecturing and Proving
Kefan Dong, Tengyu Ma
A fundamental challenge in formal theorem proving by LLMs is the lack of high-quality training data. Although reinforcement learning or expert iteration partially mitigates this is…
Formal Theorem Proving by Rewarding LLMs to Decompose Proofs Hierarchically
Kefan Dong, Arvind Mahankali, Tengyu Ma
Mathematical theorem proving is an important testbed for large language models' deep and abstract reasoning capability. This paper focuses on improving LLMs' ability to write proof…
Beyond NTK with Vanilla Gradient Descent: A Mean-Field Analysis of Neural Networks with Polynomial Width, Samples, and Time
Arvind Mahankali, Jeff Z. Haochen, Kefan Dong +2
Despite recent theoretical progress on the non-convex optimization of two-layer neural networks, it is still an open question whether gradient descent on neural networks without un…
Toward -recovery of Nonlinear Functions: A Polynomial Sample Complexity Bound for Gaussian Random Fields
Kefan Dong, Tengyu Ma
Many machine learning applications require learning a function with a small worst-case error over the entire input domain, that is, the -error, whereas most existing theo…