JEPA world models vs autoregressive LLMs and representation collapse in self-supervised learning — Interactive Knowledge Map
JEPA world models vs autoregressive LLMs and representation collapse in self-supervised learning
Key Concepts
Autoregressive LLMs
These models predict the next token in a sequence based on previous tokens, excelling in language generation but facing challenges in robust world understanding.
In the context of the query, autoregressive LLMs represent a dominant paradigm that learns representations by predicting future elements sequentially. Their 'world model' is implicitly learned through vast text data, making them powerful for language tasks but potentially less efficient or robust for general world modeling compared to approaches like JEPA, and they can also suffer from issues related to their predictive objective.
JEPA World Models
Joint Embedding Predictive Architectures (JEPA) aim to learn representations by predicting only the missing parts of a future observation, focusing on abstract features rather than pixel-level details.
JEPA models, particularly exemplified by Meta's I-JEPA, offer an alternative to autoregressive models by learning a 'world model' through non-generative self-supervised prediction. This approach is hypothesized to lead to more robust, abstract, and semantically meaningful representations, potentially making them less prone to representation collapse and more efficient for general world understanding than token-by-token prediction.
Self-Supervised Learning
SSL is a machine learning paradigm where models learn meaningful representations from unlabeled data by solving pretext tasks derived from the data itself.
Both JEPA and autoregressive LLMs (when pre-training) fundamentally rely on self-supervised learning to acquire their 'world knowledge' or representations without explicit human labels. Understanding SSL is crucial because it provides the common ground where both architectures operate and where phenomena like representation collapse emerge, making it a foundational concept for comparing their efficacy and robustness.
Representation Collapse
Representation collapse is a common failure mode in self-supervised learning where a model learns trivial or degenerate representations, mapping all inputs to the same or a very limited set of outputs.
This phenomenon is critical to the query because it highlights a major challenge in training self-supervised models, including both JEPA and autoregressive LLMs. Understanding how and why representation collapse occurs is essential for appreciating the design choices in architectures like JEPA, which specifically aim to mitigate this problem by focusing on non-generative prediction and avoiding trivial solutions, thereby learning more diverse and useful features.
World Models
World models are internal computational representations within an AI system that predict future states or outcomes based on current actions and perceptions.
The concept of a 'world model' is central to comparing JEPA and autoregressive LLMs, as both implicitly or explicitly attempt to build one. While autoregressive LLMs learn an implicit world model focused on linguistic sequences, JEPA aims to build a more abstract, feature-based world model designed for general understanding and planning, making their fundamental approaches to representing reality distinct and a key point of comparison.
