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Methods › General › Learning Rate Schedules › Cosine Annealing

Cosine Annealing

3,965 papers tagged archive 2025-07-28

Introduced by Ilya Loshchilov et al. in SGDR: Stochastic Gradient Descent with Warm Restarts

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

Cosine Annealing is a type of learning rate schedule that has the effect of starting with a large learning rate that is relatively rapidly decreased to a minimum value before being increased rapidly again. The resetting of the learning rate acts like a simulated restart of the learning process and the re-use of good weights as the starting point of the restart is referred to as a "warm restart" in contrast to a "cold restart" where a new set of small random numbers may be used as a starting point.

ηₜ = ηₘᵢₙⁱ + 1/2(ηₘₐₓⁱ-ηₘᵢₙⁱ)(1+cos((T_(cur))/Tᵢπ))

Where where ηₘᵢₙⁱ and ηₘₐₓⁱ are ranges for the learning rate, and T_(cur) account for how many epochs have been performed since the last restart.

Text Source: Jason Brownlee

Image Source: Gao Huang

PaperSource

Papers archive 2025-07-28

30 shown of 3,965, 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 1,053 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 Modelling800
Language Modeling591
Question Answering303
Large Language Model298
Text Generation285
Retrieval203
Sentence168
In-Context Learning162
Prompt Engineering133
Few-Shot Learning111
Decoder109
Code Generation106
Decision Making102
Text Classification91
RAG90
Translation89
GPU88
Retrieval-augmented Generation88
Transfer Learning88
Math87

Usage over time archive 2025-07-28

Papers per year tagged with Cosine Annealing: 2016 to 2025, peak 1,385 1,385 0 2016: 2 papers 2016 2017: 0 papers 2017 2018: 9 papers 2018 2019: 95 papers 2019 2020: 188 papers 2020 2021: 308 papers 2021 2022: 412 papers 2022 2023: 1196 papers 2023 2024: 1385 papers 2024 2025: 370 papers 2025
Papers per year the archive tags with this method, by the paper's archive date (3,965 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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