title: Uncovering mesa-optimization algorithms in Transformers
summary: 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.
paper_authors._id: 65010ea2a5c11e3a9a15d1a1
paper_authors.name: Johannes von Oswald
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-09-14T11:51:04.512Z
paper_authors._id: 6502de229421d71875f52170
paper_authors.avatarUrl: /avatars/cff4c35bb8f5704d47bd8a74e7f605e6.svg
paper_authors.fullname: Johannes von Oswald
paper_authors.type: user
paper_authors.user: voswaldj
paper_authors._id: 65010ea2a5c11e3a9a15d1a2
paper_authors.name: Eyvind Niklasson
paper_authors._id: 65010ea2a5c11e3a9a15d1a3
paper_authors.name: Maximilian Schlegel
paper_authors._id: 65010ea2a5c11e3a9a15d1a4
paper_authors.name: Seijin Kobayashi
paper_authors._id: 65010ea2a5c11e3a9a15d1a5
paper_authors.name: Nicolas Zucchet
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-24T11:25:34.905Z
paper_authors._id: 680a081bc9c06490bc2a3f9c
paper_authors.avatarUrl: /avatars/2f0a8abb30d28f9f0563d4968668f7aa.svg
paper_authors.fullname: Nicolas Zucchet
paper_authors.type: user
paper_authors.user: nzucchet
paper_authors._id: 65010ea2a5c11e3a9a15d1a6
paper_authors.name: Nino Scherrer
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-09-15T13:20:23.430Z
paper_authors._id: 643dc14d37ca9134b589bfe0
paper_authors.avatarUrl: /avatars/998977d708f205dd5df430705e55ebc9.svg
paper_authors.fullname: Nino Scherrer
paper_authors.type: user
paper_authors.user: ninoscherrer
paper_authors._id: 65010ea2a5c11e3a9a15d1a7
paper_authors.name: Nolan Miller
paper_authors._id: 65010ea2a5c11e3a9a15d1a8
paper_authors.name: Mark Sandler
paper_authors._id: 65010ea2a5c11e3a9a15d1a9
paper_authors.name: Blaise Agüera y Arcas
paper_authors._id: 65010ea2a5c11e3a9a15d1aa
paper_authors.name: Max Vladymyrov
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2023-09-14T12:37:24.813Z
paper_authors._id: 6502fe0bb1792803da806d42
paper_authors.avatarUrl: /avatars/60280ce59f1f0d67e4210b0453039282.svg
paper_authors.fullname: Max Vladymyrov
paper_authors.type: user
paper_authors.user: gozzo87
paper_authors._id: 65010ea2a5c11e3a9a15d1ab
paper_authors.name: Razvan Pascanu
paper_authors._id: 65010ea2a5c11e3a9a15d1ac
paper_authors.name: João Sacramento
paper_title: Uncovering mesa-optimization algorithms in Transformers
paper_summary: 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.
paper_ai_summary: 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.
paper_ai_keywords: 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