66 citations · 130 across the 22 of their papers we have counts for
4 papers · 1 filter
Data for Mathematical Copilots: Better Ways of Presenting Proofs for Machine Learning
Simon Frieder, Jonas Bayer, Sam Looi +13
The datasets and benchmarks commonly used to train and evaluate the mathematical capabilities of AI-based mathematical copilots (primarily large language models) exhibit several sh…
When Do Skills Help Reinforcement Learning? A Theoretical Analysis of Temporal Abstractions
Zhening Li, Gabriel Poesia, Armando Solar-Lezama
Skills are temporal abstractions that are intended to improve reinforcement learning (RL) performance through hierarchical RL. Despite our intuition about the properties of an envi…
Hypothesis Search: Inductive Reasoning with Language Models
Ruocheng Wang, Eric Zelikman, Gabriel Poesia +3
Inductive reasoning is a core problem-solving capacity: humans can identify underlying principles from a few examples, which robustly generalize to novel scenarios. Recent work eva…
Synchromesh: Reliable code generation from pre-trained language models
Gabriel Poesia, Oleksandr Polozov, Vu Le +4
Large pre-trained language models have been used to generate code,providing a flexible interface for synthesizing programs from natural language specifications. However, they often…