---
base_model: meta-llama/Llama-3.2-1B-Instruct
datasets:
- whynlp/gsm8k-aug
library_name: transformers
license: llama3.2
tags: []
pipeline_tag: text-generation
---

# Learning When to Stop: Adaptive Latent Reasoning via Reinforcement Learning

This repository contains the model presented in the paper [Learning When to Stop: Adaptive Latent Reasoning via Reinforcement Learning](https://huggingface.co/papers/2511.21581).

Latent reasoning represents a new approach in Transformer language models, aiming to compress reasoning lengths. This model uses a post-SFT reinforcement-learning methodology to optimize latent reasoning length, minimizing it while maintaining accuracy.

Code: [https://github.com/apning/adaptive-latent-reasoning](https://github.com/apning/adaptive-latent-reasoning)

## Sample Usage

You can load these models using the `automodelforcausallm_from_pretrained_latent` function from `src.model_creation`.

```python
from transformers import AutoTokenizer
from src.model_creation import automodelforcausallm_from_pretrained_latent

repo_id = "Lapisbird/Llama-adaLR-model-latent-6"

model = automodelforcausallm_from_pretrained_latent(repo_id)
tokenizer = AutoTokenizer.from_pretrained(repo_id)
```