14 citations · 24 across the 7 of their papers we have counts for
5 papers · 1 filter
On scalable oversight with weak LLMs judging strong LLMs
Zachary Kenton, Noah Y. Siegel, János Kramár +8
Scalable oversight protocols aim to enable humans to accurately supervise superhuman AI. In this paper we study debate, where two AI's compete to convince a judge; consultancy, whe…
From Next-Token to Mathematics: The Learning Dynamics of Mathematical Reasoning in Language Models
Shubhra Mishra, Gabriel Poesia, Noah D. Goodman
Large Language Models (LLMs) solely trained on next-token prediction learn to solve a wide range of problems involving mathematical reasoning. But how does this ability evolve duri…
Learning Formal Mathematics From Intrinsic Motivation
Gabriel Poesia, David Broman, Nick Haber +1
How did humanity coax mathematics from the aether? We explore the Platonic view that mathematics can be discovered from its axioms - a game of conjecture and proof. We describe Min…
Stream of Search (SoS): Learning to Search in Language
Kanishk Gandhi, Denise Lee, Gabriel Grand +4
Language models are rarely shown fruitful mistakes while training. They then struggle to look beyond the next token, suffering from a snowballing of errors and struggling to predic…
Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking
Eric Zelikman, Georges Harik, Yijia Shao +3
When writing and talking, people sometimes pause to think. Although reasoning-focused works have often framed reasoning as a method of answering questions or completing agentic tas…