Practical overview
When it fits
Use Rlt when you want to work directly with a recurrent-looped transformer implementation in Python, including its documented loss, generation, and recurrence controls.
Who it is for
- Python and PyTorch developers exploring recurrent transformer models.
- Researchers or practitioners experimenting with sequence modeling, token generation, or recurrent block configurations.
Documented uses
- Calculate training loss for token sequences and run backpropagation.
- Generate additional tokens from a prompt.
- Vary recurrent block lengths across a sequence.
- Compare a training setup with or without the optional next-latent prediction loss.
Requirements & limits
The repository is public and identifies the project as MIT-licensed. Its documented installation command is `pip install rlt-pytorch`.
- The implementation exposes configurable encoder and decoder depth, decoder sliding-window size, and an optional truncated backpropagation-through-time step size.
- Next-latent prediction loss is an optional model setting.
- Variable recurrent lengths must sum to the input sequence length; during generation, recurrent state advances at block boundaries.
- The README documents an enwik8 training command but does not provide a complete text-tokenization workflow.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Calculate training loss and generate a token sequence
Use the documented RLT methods to calculate model loss and extend a token prompt.
Before you start
- Install the package with
pip install rlt-pytorch. - Have PyTorch available and tokenized inputs whose IDs fit the configured
num_tokensvocabulary.
Steps
Import
torchandRLTfromRLT.Construct an RLT model with
num_tokens,dim,enc_depth,dec_depth, anddec_sliding_window_sizeconfigured for the intended inputs.Pass a token tensor to
model(tokens, return_loss=True)and callloss.backward()for the shown training-loss flow.Pass a token prompt to
model.generate(prompt, max_len=128)to generate additional tokens.
Expected outcome
A computed training loss with a backward pass, and a generated token sequence extending the supplied prompt.
Guide limitations
- The examples use randomly generated token IDs and do not document text tokenization or corpus preparation.
- The README does not specify model checkpoints, evaluation, or production deployment.
Train with variable recurrent block lengths
Pass explicit recurrent block lengths to the documented training-loss call.
Before you start
- Install
rlt-pytorchand use the PyTorch RLT implementation. - Prepare a token sequence whose length equals the sum of the recurrent block lengths.
Steps
Define a sequence of block lengths, such as
(1, 2, 5, 2, 1, 3).Create a token tensor with sequence length equal to the sum of those lengths.
Call
model(tokens, return_loss=True, recurrent_lengths=block_lengths)and useloss.backward()as shown.
Expected outcome
A training loss is calculated using the supplied recurrent block layout.
Guide limitations
- The block lengths must sum to the sequence length.
- The README notes that during generation the recurrent state advances only at block boundaries.
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 14 October 2026
- Original website title
- lucidrains/RLT · Official homepage title · retained in its source language