license: mit
base_model: openai-community/gpt2
language:
- en
tags:
- latent-reasoning
- interpretability
- reasoning
- multimode_codi
- prosqa
Multi-mode CODI · gpt2 · ProsQA
This is the Multi-mode CODI checkpoint trained on ProsQA with base model
openai-community/gpt2, from the paper
Are Latent Reasoning Models Easily Interpretable? (Dilgren & Wiegreffe, 2026).
- 📄 Paper: https://arxiv.org/abs/2604.04902
- 💻 Code: https://github.com/connordilgren/are-lrms-easily-interpretable
- 📚 Collection (all checkpoints): https://huggingface.co/collections/connordilgren/are-latent-reasoning-models-easily-interpretable-6a46a3c39b0045c223b15a89
Files
This repository contains a single raw PyTorch checkpoint, pytorch_model.bin — the state dict as
saved by the training framework. It is not a from_pretrained-style model; it is loaded by
the paper's evaluation code, which builds the base model and applies this checkpoint.
Usage
The evaluation code in the repository loads this checkpoint from the local path
configured in model_paths.yaml. Download it to the expected location with:
hf download connordilgren/gpt2-prosqa-multimode-codi pytorch_model.bin --local-dir checkpoints/codi_trained_models/prosqa_gpt2_direct_answer/gpt2/ep_40/lr_0.003/seed_11
This places the file at checkpoints/codi_trained_models/prosqa_gpt2_direct_answer/gpt2/ep_40/lr_0.003/seed_11/pytorch_model.bin, which is the path referenced for this model
(gpt2 → prosqa → multimode_codi) in model_paths.yaml. See the
repository README for full setup and evaluation instructions.
Citation
@misc{dilgren2026latentreasoningmodelseasily,
title={Are Latent Reasoning Models Easily Interpretable?},
author={Connor Dilgren and Sarah Wiegreffe},
year={2026},
eprint={2604.04902},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2604.04902},
}