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

# Llama-adaLR-model-latent-6: Learning When to Stop: Adaptive Latent Reasoning via Reinforcement Learning

This repository contains the `Llama-adaLR-model-latent-6` model, presented in the paper "[Learning When to Stop: Adaptive Latent Reasoning via Reinforcement Learning](https://huggingface.co/papers/2511.21581)".

This model introduces adaptive-length latent reasoning, a novel approach to optimizing the reasoning length of Transformer language models. By leveraging a post-SFT reinforcement-learning methodology, it aims to minimize reasoning length while maintaining accuracy. Experiments on the Llama 3.2 1B model and the GSM8K-Aug dataset demonstrated a 52% drop in total reasoning length without sacrificing accuracy.

For more details, including additional model weights and ongoing developments, please refer to the official [GitHub repository](https://github.com/apning/adaptive-latent-reasoning).

## Sample Usage

You can load these models using the function `automodelforcausallm_from_pretrained_latent` from `src.model_creation` with the `transformers` library, as shown in the following example found in the GitHub repository:

```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)
```