yingfanbot/gsm-lotus-llama3b-codi

gsm-lotus-llama3b-codi

LOTUS + CODI (adds CODI trajectory distillation) 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 variant was trained with an extra CODI's trajectory-level distillation loss.

• —GSM8K (GSM8k-Aug) test accuracy: 70.58% (931/1319)
• —Latent config: K = 6 blocks, cthought = 25 tokens/block, nlooped_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-codi \
  --datasets gsm8k --n_looped_iters 6 --c_thought 25 --bf16

Yields 70.58% 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}
}