AI models
aLSTM(depth-2)+RecurrentPolicy (WT2)
Training compute
7.3×10¹⁶ FLOP
Parameters
32M
Published
May 22, 2018
aLSTM(depth-2)+RecurrentPolicy (WT2) is an AI model developed by University of Manchester and Alan Turing Institute (United Kingdom), first published in May 2018. It works in the language domain, on tasks such as language modeling.
Training it took an estimated 7.3×10¹⁶ FLOP of compute (estimation method: operation counting). The model has 32,000,000 parameters. It was trained on roughly 2M datapoints.
Access: Unreleased. Its weights are not openly released. The reference paper has 12 citations. Epoch AI rates the confidence of this record as confident.
Full record
- Organization
- University of Manchester, Alan Turing Institute
- Country of organization
- United Kingdom
- Domain
- Language
- Task
- Language modeling
- Training compute
- 7.3×10¹⁶ FLOP
- Compute estimation method
- Operation counting
- Parameters
- 32,000,000
- Dataset size
- 2M
- Model accessibility
- Unreleased
- Open weights
- No
- Citations
- 12
- Epoch confidence
- Confident
SourceEpoch AI, 'AI Models'. Published online at epoch.ai. Retrieved 2026-07-29 from https://epoch.ai/data/ai-models. Licensed under CC BY 4.0.
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