Verified75% confidenceFactExact time
David Ha and Schmidhuber published the paper "World Models" in 2018, demonstrating how an agent can complete reinforcement learning training within dreams
5
Sources
75%
Confidence
Long-term
Relevance
7/11/2026
First Seen
Sources
Lingbot World: A 14B Open-Weight World Model with Causal Architecture and Inference Acceleration
redditr/StableDiffusion7/9/2026
Related Claims
VerifiedThe 2018 paper "World Models" by David Ha and Jürgen Schmidhuber formally introduced the concept of world models into the field of deep learning.79% similarUnverifiedThe idea of world models can be traced back to the field of reinforcement learning, where agents use world models to train through "imagination" to reduce the cost of interacting with the real environment.67% similarVerifiedThe concept of world models originates from cognitive science and reinforcement learning, strongly promoted by Yann LeCun and others, referring to an agent's internal representation of the external environment.65% similarUnverifiedThe concept of world models was systematically articulated as early as 1943 by Kenneth Craik in The Nature of Explanation, and was later integrated into the field of reinforcement learning.64% similarUnverifiedThe alternative path toward artificial general intelligence proposed by LeCun is a self-supervised learning architecture driven by a 'World Model', such as JEPA64% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/477992API
curl https://kongchang.com/api/v1/knowledge/claims/477992MCP
get_claim(id=477992)