Methods › General › Learning Rate Schedules › Linear Warmup

Linear Warmup

38 papers tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Linear Warmup is a learning rate schedule where we linearly increase the learning rate from a low rate to a constant rate thereafter. This reduces volatility in the early stages of training.

Image Credit: Chengwei Zhang

Papers archive 2025-07-28

30 shown of 38, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

20 shown of 79 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Language Modelling7
Representation Learning6
Text Generation5
Language Modeling4
Object3
Object Detection3
Self-Supervised Learning3
Adversarial Robustness2
Backdoor Attack2
GPU2
Image Classification2
Reinforcement Learning (RL)2
Segmentation2
Semantic Segmentation2
object-detection2
3D Object Detection1
Action Classification1
Action Recognition1
Attribute1
Authorship Attribution1

Usage over time archive 2025-07-28

Papers per year tagged with Linear Warmup: 2017 to 2025, peak 9 9 0 2017: 1 paper 2017 2018: 1 paper 2018 2019: 4 papers 2019 2020: 2 papers 2020 2021: 5 papers 2021 2022: 9 papers 2022 2023: 6 papers 2023 2024: 6 papers 2024 2025: 4 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (38 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Learning Rate Schedules

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