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Implicit bias of AdamW: ℓ∞ norm constrained optimization.arXiv preprint arXiv:2404.04454

4 Pith papers cite this work. Polarity classification is still indexing.

4 Pith papers citing it

citation-role summary

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citation-polarity summary

fields

cs.LG 3 cs.AI 1

years

2026 2 2025 2

verdicts

UNVERDICTED 4

roles

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polarities

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representative citing papers

Training Deep Learning Models with Norm-Constrained LMOs

cs.LG · 2025-02-11 · unverdicted · novelty 7.0

Scion is a new stochastic LMO-based optimizer family that unifies existing methods, supports unconstrained problems, and delivers hyperparameter transferability plus speedups on nanoGPT training.

Optimistic Dual Averaging Unifies Modern Optimizers

cs.LG · 2026-05-11 · unverdicted · novelty 6.0

SODA unifies several modern optimizers under optimistic dual averaging and supplies a 1/k decay wrapper that improves performance without weight decay tuning.

Demystifying Manifold Constraints in LLM Pre-training

cs.LG · 2026-05-06 · unverdicted · novelty 6.0

Manifold constraints via the new MACRO optimizer independently bound activation scales and enforce rotational equilibrium in LLM pre-training, subsuming RMS normalization and decoupled weight decay while delivering competitive performance with convergence guarantees.

Hierarchical Reasoning Model

cs.AI · 2025-06-26 · unverdicted · novelty 5.0

HRM is a recurrent architecture with high-level planning and low-level execution modules that reaches near-perfect accuracy on complex Sudoku, maze navigation, and ARC benchmarks using 27M parameters and 1000 samples without pre-training or CoT supervision.

citing papers explorer

Showing 4 of 4 citing papers.

  • Training Deep Learning Models with Norm-Constrained LMOs cs.LG · 2025-02-11 · unverdicted · none · ref 211

    Scion is a new stochastic LMO-based optimizer family that unifies existing methods, supports unconstrained problems, and delivers hyperparameter transferability plus speedups on nanoGPT training.

  • Optimistic Dual Averaging Unifies Modern Optimizers cs.LG · 2026-05-11 · unverdicted · none · ref 17

    SODA unifies several modern optimizers under optimistic dual averaging and supplies a 1/k decay wrapper that improves performance without weight decay tuning.

  • Demystifying Manifold Constraints in LLM Pre-training cs.LG · 2026-05-06 · unverdicted · none · ref 30

    Manifold constraints via the new MACRO optimizer independently bound activation scales and enforce rotational equilibrium in LLM pre-training, subsuming RMS normalization and decoupled weight decay while delivering competitive performance with convergence guarantees.

  • Hierarchical Reasoning Model cs.AI · 2025-06-26 · unverdicted · none · ref 51

    HRM is a recurrent architecture with high-level planning and low-level execution modules that reaches near-perfect accuracy on complex Sudoku, maze navigation, and ARC benchmarks using 27M parameters and 1000 samples without pre-training or CoT supervision.