With the widespread adoption of deep learning, reinforcement learning (RL) has
experienced a dramatic increase in popularity, scaling to previously intractable problems …

Abstract Language models (LMs) are pretrained to imitate text from large and diverse
datasets that contain content that would violate human preferences if generated by an LM …

Recent improvements in conditional generative modeling have made it possible to generate
high-quality images from language descriptions alone. We investigate whether these …

Nowadays, open-source large language models like LLaMA have emerged. Recent
developments have incorporated supervised fine-tuning (SFT) and reinforcement learning …

In reinforcement learning (RL), it is easier to solve a task if given a good representation.
While deep RL should automatically acquire such good representations, prior work often …

Online updating of time series forecasting models aims to address the concept drifting
problem by efficiently updating forecasting models based on streaming data. Many …

We show that domain-general automatic evaluators can significantly improve the
performance of agents for web navigation and device control. We experiment with multiple …

Several recent works have proposed a class of algorithms for the offline reinforcement
learning (RL) problem that we will refer to as return-conditioned supervised learning …

Abstract We introduce Masked Trajectory Models (MTM) as a generic abstraction for
sequential decision making. MTM takes a trajectory, such as a state-action sequence, and …

Recent works have shown that tackling offline reinforcement learning (RL) with a conditional
policy produces promising results. The Decision Transformer (DT) combines the conditional …