Jessy Lin Learning to Model the World With Language 2024
[TOC] Title: Learning to Model the World With Language 2024 Author: Jessy Lin et. al. Publish Year: ICML 2024 Review Date: Fri, Jun 21, 2024 url: https://arxiv.org/abs/2308.01399 Summary of paper Motivation in this work, we propose that agents can ground diverse kinds of language by using it to predict the future in contrast to directly predicting what to do with a language-conditioned policy, Dynalang decouples learning to model the world with language (supervised learning with prediction objectives) from learning to act given that model (RL with task rewards) Future prediction provides a rich grounding signal for learning what language utterances mean, which in turn equip the agent with a richer understanding of the world to solve complex tasks....