2.3k citations · 4.2k across the 44 of their papers we have counts for
9 papers · 1 filter
Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs
Albert Q. Jiang, Sean Welleck, Jin Peng Zhou +6
The formalization of existing mathematical proofs is a notoriously difficult process. Despite decades of research on automation and proof assistants, writing formal proofs remains…
Fast and Precise: Adjusting Planning Horizon with Adaptive Subgoal Search
Michał Zawalski, Michał Tyrolski, Konrad Czechowski +6
Complex reasoning problems contain states that vary in the computational cost required to determine a good action plan. Taking advantage of this property, we propose Adaptive Subgo…
Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers
Albert Q. Jiang, Wenda Li, Szymon Tworkowski +5
In theorem proving, the task of selecting useful premises from a large library to unlock the proof of a given conjecture is crucially important. This presents a challenge for all t…
Subgoal Search For Complex Reasoning Tasks
Konrad Czechowski, Tomasz Odrzygóźdź, Marek Zbysiński +5
Humans excel in solving complex reasoning tasks through a mental process of moving from one idea to a related one. Inspired by this, we propose Subgoal Search (kSubS) method. Its k…
Proof Artifact Co-training for Theorem Proving with Language Models
Jesse Michael Han, Jason Rute, Yuhuai Wu +2
Labeled data for imitation learning of theorem proving in large libraries of formalized mathematics is scarce as such libraries require years of concentrated effort by human specia…
INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving
Yuhuai Wu, Albert Qiaochu Jiang, Jimmy Ba +1
In learning-assisted theorem proving, one of the most critical challenges is to generalize to theorems unlike those seen at training time. In this paper, we introduce INT, an INequ…
Concurrent Meta Reinforcement Learning
Emilio Parisotto, Soham Ghosh, Sai Bhargav Yalamanchi +3
State-of-the-art meta reinforcement learning algorithms typically assume the setting of a single agent interacting with its environment in a sequential manner. A negative side-effe…
Some Considerations on Learning to Explore via Meta-Reinforcement Learning
Bradly C. Stadie, Ge Yang, Rein Houthooft +5
We consider the problem of exploration in meta reinforcement learning. Two new meta reinforcement learning algorithms are suggested: E-MAML and E-. Results are present…
An Empirical Analysis of Proximal Policy Optimization with Kronecker-factored Natural Gradients
Jiaming Song, Yuhuai Wu
In this technical report, we consider an approach that combines the PPO objective and K-FAC natural gradient optimization, for which we call PPOKFAC. We perform a range of empirica…