Cognitively Inspired Energy-Based World Models

• URL: http://arxiv.org/abs/2406.08862v1
• Date: Thu, 13 Jun 2024 06:54:37 GMT
• Title: Cognitively Inspired Energy-Based World Models
• Authors: Alexi Gladstone, Ganesh Nanduru, Md Mofijul Islam, Aman Chadha, Jundong Li, Tariq Iqbal,
• Abstract summary: We introduce Energy-Based World Models (EBWM) EBWM involves training an Energy-Based Model (EBM) to predict the compatibility of a given context and a predicted future state. We develop a variant of the traditional autoregressive transformer termed the Energy-Based Transformer (EBT)
• Score: 40.08174759225766
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: One of the predominant methods for training world models is autoregressive prediction in the output space of the next element of a sequence. In Natural Language Processing (NLP), this takes the form of Large Language Models (LLMs) predicting the next token; in Computer Vision (CV), this takes the form of autoregressive models predicting the next frame/token/pixel. However, this approach differs from human cognition in several respects. First, human predictions about the future actively influence internal cognitive processes. Second, humans naturally evaluate the plausibility of predictions regarding future states. Based on this capability, and third, by assessing when predictions are sufficient, humans allocate a dynamic amount of time to make a prediction. This adaptive process is analogous to System 2 thinking in psychology. All these capabilities are fundamental to the success of humans at high-level reasoning and planning. Therefore, to address the limitations of traditional autoregressive models lacking these human-like capabilities, we introduce Energy-Based World Models (EBWM). EBWM involves training an Energy-Based Model (EBM) to predict the compatibility of a given context and a predicted future state. In doing so, EBWM enables models to achieve all three facets of human cognition described. Moreover, we developed a variant of the traditional autoregressive transformer tailored for Energy-Based models, termed the Energy-Based Transformer (EBT). Our results demonstrate that EBWM scales better with data and GPU Hours than traditional autoregressive transformers in CV, and that EBWM offers promising early scaling in NLP. Consequently, this approach offers an exciting path toward training future models capable of System 2 thinking and intelligently searching across state spaces.

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