README.md

---
license: mit
base_model: meta-llama/Llama-3.2-1B-Instruct
language:
  - en
tags:
  - latent-reasoning
  - interpretability
  - reasoning
  - multimode_codi
  - prosqa
---

# Multi-mode CODI · llama32-1b · ProsQA

This is the **Multi-mode CODI** checkpoint trained on **ProsQA** with base model
[`meta-llama/Llama-3.2-1B-Instruct`](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct), from the paper
[*Are Latent Reasoning Models Easily Interpretable?*](https://arxiv.org/abs/2604.04902) (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](https://github.com/connordilgren/are-lrms-easily-interpretable) loads this checkpoint from the local path
configured in `model_paths.yaml`. Download it to the expected location with:

```bash
hf download connordilgren/llama32-1b-prosqa-multimode-codi pytorch_model.bin --local-dir checkpoints/codi_trained_models/prosqa_llama1b_direct_answer/Llama-3.2-1B-Instruct/ep_10/lr_0.0008/seed_11
```

This places the file at `checkpoints/codi_trained_models/prosqa_llama1b_direct_answer/Llama-3.2-1B-Instruct/ep_10/lr_0.0008/seed_11/pytorch_model.bin`, which is the path referenced for this model
(`llama` → `prosqa` → `multimode_codi`) in `model_paths.yaml`. See the
repository README for full setup and evaluation instructions.

## Citation

```bibtex
@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},
}
```