Search Results for author:
Found 10 papers, 7 papers with code
Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision
Widely used alignment techniques, such as reinforcement learning from human feedback (RLHF), rely on the ability of humans to supervise model behavior - for example, to evaluate whether a model faithfully followed instructions or generated safe outputs.
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)
Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos
Pretraining on noisy, internet-scale datasets has been heavily studied as a technique for training models with broad, general capabilities for text, images, and other modalities.
Multi-task curriculum learning in a complex, visual, hard-exploration domain: Minecraft
An important challenge in reinforcement learning is training agents that can solve a wide variety of tasks.
Emergent Reciprocity and Team Formation from Randomized Uncertain Social Preferences
Multi-agent reinforcement learning (MARL) has shown recent success in increasingly complex fixed-team zero-sum environments.
Emergent Tool Use From Multi-Agent Autocurricula
Through multi-agent competition, the simple objective of hide-and-seek, and standard reinforcement learning algorithms at scale, we find that agents create a self-supervised autocurriculum inducing multiple distinct rounds of emergent strategy, many of which require sophisticated tool use and coordination.
Learning Dexterous In-Hand Manipulation
We use reinforcement learning (RL) to learn dexterous in-hand manipulation policies which can perform vision-based object reorientation on a physical Shadow Dexterous Hand.
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
The purpose of this technical report is two-fold.
Accelerating Neural Architecture Search using Performance Prediction
Methods for neural network hyperparameter optimization and meta-modeling are computationally expensive due to the need to train a large number of model configurations.
Designing Neural Network Architectures using Reinforcement Learning
We introduce MetaQNN, a meta-modeling algorithm based on reinforcement learning to automatically generate high-performing CNN architectures for a given learning task.
