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Benchmarking World Models for Continual Learning on Compositional Tasks

Paper recorded by Signals 4 on 2026-09-18 in cs.LG. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-18 on arXiv · recorded by Signals 4 on 2026-09-21

Category: cs.LG · 机器学习 · first seen 2026-09-21

Abstract

A desirable property of a world model is the ability to learn continually across tasks, adapting to new environments without forgetting what the agent has already learnt. In particular, the ability to retain and reuse knowledge obtained from prior experiences underpins an agent's ability to efficiently adapt to novel environments, as the dynamics of the physical world can often be described in rec

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#3 most recent of 235 cs.LG papers we have recorded · ↑ newer: Predictable Failure in Multi-Hop Retrieval: Score-Distributional Confi · ↓ older: Particle Competition and Cooperation for Robust Graph Convolutional Ne
Cite this page: Benchmarking World Models for Continual Learning on Compositional Tasks: the #3 most recent of 235 cs.LG papers we have recorded (as of 2026-09-18). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/benchmarking-world-models-for-continual-learning-on-compositional-tasks.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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