Uncovering mesa-optimization algorithms in Transformers
Transformers have become the dominant model in deep learning, but the reason
for their superior performance is poorly understood. Here, we hypothesize that
the strong performance of Transformers stems from an architectural bias towards
mesa-optimization, a learned process running within the forward pass of a model
consisting of the following two steps: (i) the construction of an internal
learning objective, and (ii) its corresponding solution found through
optimization. To test this hypothesis, we reverse-engineer a series of
autoregressive Transformers trained on simple sequence modeling tasks,
uncovering underlying gradient-based mesa-optimization algorithms driving the
generation of predictions. Moreover, we show that the learned forward-pass
optimization algorithm can be immediately repurposed to solve supervised
few-shot tasks, suggesting that mesa-optimization might underlie the in-context
learning capabilities of large language models. Finally, we propose a novel
self-attention layer, the mesa-layer, that explicitly and efficiently solves
optimization problems specified in context. We find that this layer can lead to
improved performance in synthetic and preliminary language modeling
experiments, adding weight to our hypothesis that mesa-optimization is an
important operation hidden within the weights of trained Transformers.
65010ea2a5c11e3a9a15d1a1
Johannes von Oswald
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2023-09-14T11:51:04.512Z
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Johannes von Oswald
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voswaldj
65010ea2a5c11e3a9a15d1a2
Eyvind Niklasson
65010ea2a5c11e3a9a15d1a3
Maximilian Schlegel
65010ea2a5c11e3a9a15d1a4
Seijin Kobayashi
65010ea2a5c11e3a9a15d1a5
Nicolas Zucchet
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2025-04-24T11:25:34.905Z
680a081bc9c06490bc2a3f9c
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Nicolas Zucchet
user
nzucchet
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Nino Scherrer
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2023-09-15T13:20:23.430Z
643dc14d37ca9134b589bfe0
/avatars/998977d708f205dd5df430705e55ebc9.svg
Nino Scherrer
user
ninoscherrer
65010ea2a5c11e3a9a15d1a7
Nolan Miller
65010ea2a5c11e3a9a15d1a8
Mark Sandler
65010ea2a5c11e3a9a15d1a9
Blaise Agüera y Arcas
65010ea2a5c11e3a9a15d1aa
Max Vladymyrov
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2023-09-14T12:37:24.813Z
6502fe0bb1792803da806d42
/avatars/60280ce59f1f0d67e4210b0453039282.svg
Max Vladymyrov
user
gozzo87
65010ea2a5c11e3a9a15d1ab
Razvan Pascanu
65010ea2a5c11e3a9a15d1ac
João Sacramento
Uncovering mesa-optimization algorithms in Transformers
Transformers have become the dominant model in deep learning, but the reason
for their superior performance is poorly understood. Here, we hypothesize that
the strong performance of Transformers stems from an architectural bias towards
mesa-optimization, a learned process running within the forward pass of a model
consisting of the following two steps: (i) the construction of an internal
learning objective, and (ii) its corresponding solution found through
optimization. To test this hypothesis, we reverse-engineer a series of
autoregressive Transformers trained on simple sequence modeling tasks,
uncovering underlying gradient-based mesa-optimization algorithms driving the
generation of predictions. Moreover, we show that the learned forward-pass
optimization algorithm can be immediately repurposed to solve supervised
few-shot tasks, suggesting that mesa-optimization might underlie the in-context
learning capabilities of large language models. Finally, we propose a novel
self-attention layer, the mesa-layer, that explicitly and efficiently solves
optimization problems specified in context. We find that this layer can lead to
improved performance in synthetic and preliminary language modeling
experiments, adding weight to our hypothesis that mesa-optimization is an
important operation hidden within the weights of trained Transformers.
Transformers' superior performance might be due to mesa-optimization, an internal learning objective and its solution during the forward pass, which can be repurposed for few-shot tasks and potentially enhances language models' in-context learning.
Transformers mesa-optimization autoregressive Transformers sequence modeling gradient-based Mesa-optimization self-attention layer mesa-layer synthetic and preliminary language modeling experiments in-context learning