The Serial Scaling Hypothesis
While machine learning has advanced through massive parallelization, we
identify a critical blind spot: some problems are fundamentally sequential.
These "inherently serial" problems-from mathematical reasoning to physical
simulations to sequential decision-making-require dependent computational steps
that cannot be parallelized. Drawing from complexity theory, we formalize this
distinction and demonstrate that current parallel-centric architectures face
fundamental limitations on such tasks. We argue that recognizing the serial
nature of computation holds profound implications on machine learning, model
design, hardware development. As AI tackles increasingly complex reasoning,
deliberately scaling serial computation-not just parallel computation-is
essential for continued progress.
687fed926b4cbf38ad40e102
Yuxi Liu
687fed926b4cbf38ad40e103
Konpat Preechakul
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2025-07-23T08:36:59.205Z
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Konpat Preechakul
user
phizaz
687fed926b4cbf38ad40e104
Kananart Kuwaranancharoen
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2025-07-23T08:36:56.832Z
688046b0427402e69f8672ab
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Kananart Kuwaranancharoen
user
kkuwaran
687fed926b4cbf38ad40e105
Yutong Bai
The Serial Scaling Hypothesis
While machine learning has advanced through massive parallelization, we
identify a critical blind spot: some problems are fundamentally sequential.
These "inherently serial" problems-from mathematical reasoning to physical
simulations to sequential decision-making-require dependent computational steps
that cannot be parallelized. Drawing from complexity theory, we formalize this
distinction and demonstrate that current parallel-centric architectures face
fundamental limitations on such tasks. We argue that recognizing the serial
nature of computation holds profound implications on machine learning, model
design, hardware development. As AI tackles increasingly complex reasoning,
deliberately scaling serial computation-not just parallel computation-is
essential for continued progress.
Recognizing the inherently serial nature of certain computational tasks is crucial for advancing machine learning and model design, as parallel architectures have limitations in handling such problems.