Impact of Scale on Data-Efficient Transformer World Models for Atari
A new research paper submitted to arXiv investigates the independent impact of model scale on data-efficient, generalist transformer world models within the Atari 100k benchmark. The study addresses the challenge of developing systems with human-like data efficiency by using a minimalist transformer architecture and fixed offline datasets. Results indicate that individual gaming environments fall into distinct scaling regimes; some allow for monotonic improvements as models grow larger, while others suffer from degraded fidelity in the classical regime. However, the research demonstrates that joint training across a unified suite of 26 Atari environments stabilizes these dynamics, ensuring consistent performance gains regardless of inherent environmental differences. Furthermore, the improved model fidelity directly enhances downstream control capabilities, with policies learned in simulation achieving a median expert-random-normalized score of 0.770. These findings suggest that future advancements in artificial intelligence rely not only on architectural innovations but also on precise scaling strategies. The work highlights the importance of distinguishing between architectural mechanisms and scale effects in creating robust, generalist AI systems.
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Impact of Scale on Data-Efficient Transformer World Models for Atari
A new research paper submitted to arXiv investigates the independent impact of model scale on data-efficient, generalist transformer world models within the Atari 100k benchmark. The study addresses the challenge of developing systems with human-like data efficiency by using a minimalist transformer architecture and fixed offline datasets. Results indicate that individual gaming environments fall into distinct scaling regimes; some allow for monotonic improvements as models grow larger, while others suffer from degraded fidelity in the classical regime. However, the research demonstrates that joint training across a unified suite of 26 Atari environments stabilizes these dynamics, ensuring consistent performance gains regardless of inherent environmental differences. Furthermore, the improved model fidelity directly enhances downstream control capabilities, with policies learned in simulation achieving a median expert-random-normalized score of 0.770. These findings suggest that future advancements in artificial intelligence rely not only on architectural innovations but also on precise scaling strategies. The work highlights the importance of distinguishing between architectural mechanisms and scale effects in creating robust, generalist AI systems.
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