A delay-aware RL approach learns transferable structured representations and dynamics via implicit causal graphs, outperforming baselines on delayed DMC tasks and accelerating adaptation to new tasks.
Efficient world models with context- aware tokenization
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 3years
2026 3verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
Transformer world models on Atari exhibit game-specific scaling regimes, but joint training on 26 environments produces consistent monotonic gains that improve downstream control policies to a median normalized score of 0.770.
The paper delivers a multi-axis taxonomy for world models that maps architectures, training families, reasoning strategies, and domains from early cognitive foundations through systems such as Dreamer, MuZero, and Sora while noting evaluation gaps.
citing papers explorer
-
Transferable Delay-Aware Reinforcement Learning via Implicit Causal Graph Modeling
A delay-aware RL approach learns transferable structured representations and dynamics via implicit causal graphs, outperforming baselines on delayed DMC tasks and accelerating adaptation to new tasks.
-
Probing the Impact of Scale on Data-Efficient, Generalist Transformer World Models for Atari
Transformer world models on Atari exhibit game-specific scaling regimes, but joint training on 26 environments produces consistent monotonic gains that improve downstream control policies to a median normalized score of 0.770.
-
World Models: A Comprehensive Survey of Architectures, Methodologies, Reasoning Paradigms, and Applications
The paper delivers a multi-axis taxonomy for world models that maps architectures, training families, reasoning strategies, and domains from early cognitive foundations through systems such as Dreamer, MuZero, and Sora while noting evaluation gaps.