gsm-lotus-llama3b
LOTUS (direct LM-head supervision, no CODI) checkpoint fine-tuned from meta-llama/Llama-3.2-3B-Instruct on GSM8k-Aug, from the paper
Bridging the Gap Between Latent and Explicit Reasoning with Looped Transformers.
This is the LOTUS model.
- GSM8K (GSM8k-Aug) test accuracy: 70.05% (924/1319)
- Latent config: K = 6 blocks, c_thought = 25 tokens/block, n_looped_iters = 6
- Base: meta-llama/Llama-3.2-3B-Instruct (vocab 128256 -> 128259 for 3 latent tokens)
Loading
from_pretrained loads the weights only — the looped padded architecture needs the LOTUS code
(code repo).
Reproduce the number above
Run in the pinned env (torch 2.7 / transformers 4.46.2). This loads the safetensors straight from this repo and runs the latent loop — no separate checkpoint file needed:
python scripts/eval.py \
--model_id yingfanbot/gsm-lotus-llama3b \
--datasets gsm8k --n_looped_iters 6 --c_thought 25 --bf16
Yields 70.05% on GSM8k-Aug (verified by loading this repo's safetensors directly).
Citation
@article{fan2026bridging,
title={Bridging the Gap Between Latent and Explicit Reasoning with Looped Transformers},
author={Fan, Ying and Svete, Anej and Lee, Kangwook},
journal={arXiv preprint arXiv:2606.31779},
year={2026}
}