Search Results for author:
Found 9 papers, 7 papers with code
OpenAI o1 System Card
The o1 model series is trained with large-scale reinforcement learning to reason using chain of thought.
Let's Verify Step by Step
We conduct our own investigation, finding that process supervision significantly outperforms outcome supervision for training models to solve problems from the challenging MATH dataset.
Ranked #1 on
Math Word Problem Solving
on MATH minival
(using extra training data)
WebGPT: Browser-assisted question-answering with human feedback
This model's answers are preferred by humans 56% of the time to those of our human demonstrators, and 69% of the time to the highest-voted answer from Reddit.
Batch size-invariance for policy optimization
We say an algorithm is batch size-invariant if changes to the batch size can largely be compensated for by changes to other hyperparameters.
Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmark
We present the design of a centralized benchmark for Reinforcement Learning which can help measure Sample Efficiency and Generalization in Reinforcement Learning by doing end to end evaluation of the training and rollout phases of thousands of user submitted code bases in a scalable way.
Phasic Policy Gradient
We introduce Phasic Policy Gradient (PPG), a reinforcement learning framework which modifies traditional on-policy actor-critic methods by separating policy and value function training into distinct phases.
Ranked #1 on
Reinforcement Learning (RL)
on ProcGen
(using extra training data)
Leveraging Procedural Generation to Benchmark Reinforcement Learning
We introduce Procgen Benchmark, a suite of 16 procedurally generated game-like environments designed to benchmark both sample efficiency and generalization in reinforcement learning.
Quantifying Generalization in Reinforcement Learning
In this paper, we investigate the problem of overfitting in deep reinforcement learning.
