4.3k citations · 4.8k across the 9 of their papers we have counts for
18 papers · 1 filter
Scaling Laws for Reward Model Overoptimization
Leo Gao, John Schulman, Jacob Hilton
In reinforcement learning from human feedback, it is common to optimize against a reward model trained to predict human preferences. Because the reward model is an imperfect proxy,…
Measuring Sample Efficiency and Generalization in Reinforcement Learning Benchmarks: NeurIPS 2020 Procgen Benchmark
Sharada Mohanty, Jyotish Poonganam, Adrien Gaidon +20
The NeurIPS 2020 Procgen Competition was designed as a centralized benchmark with clearly defined tasks for measuring Sample Efficiency and Generalization in Reinforcement Learning…
Scaling Laws for Autoregressive Generative Modeling
Tom Henighan, Jared Kaplan, Mor Katz +16
We identify empirical scaling laws for the cross-entropy loss in four domains: generative image modeling, video modeling, multimodal imagetext models, and mathemat…
Phasic Policy Gradient
Karl Cobbe, Jacob Hilton, Oleg Klimov +1
We introduce Phasic Policy Gradient (PPG), a reinforcement learning framework which modifies traditional on-policy actor-critic methods by separating policy and value function trai…
Leveraging Procedural Generation to Benchmark Reinforcement Learning
Karl Cobbe, Christopher Hesse, Jacob Hilton +1
We introduce Procgen Benchmark, a suite of 16 procedurally generated game-like environments designed to benchmark both sample efficiency and generalization in reinforcement learnin…
Policy Gradient Search: Online Planning and Expert Iteration without Search Trees
Thomas Anthony, Robert Nishihara, Philipp Moritz +2
Monte Carlo Tree Search (MCTS) algorithms perform simulation-based search to improve policies online. During search, the simulation policy is adapted to explore the most promising…