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--- |
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base_model: meta-llama/Llama-3.2-1B-Instruct |
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datasets: |
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- whynlp/gsm8k-aug |
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library_name: transformers |
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license: llama3.2 |
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tags: [] |
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pipeline_tag: text-generation |
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--- |
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# Learning When to Stop: Adaptive Latent Reasoning via Reinforcement Learning |
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This repository contains the weights for **Adaptive Latent Reasoning models**, as introduced in the paper [Learning When to Stop: Adaptive Latent Reasoning via Reinforcement Learning](https://huggingface.co/papers/2511.21581). |
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Latent reasoning represents a new development in Transformer language models that has shown potential in compressing reasoning lengths compared to chain-of-thought reasoning. This work develops adaptive-length latent reasoning models and introduces a post-SFT reinforcement-learning methodology to optimize latent reasoning length by minimizing reasoning length while maintaining accuracy. Experiments on the Llama 3.2 1B model and the GSM8K-Aug dataset showed a 52% drop in total reasoning length with no penalty to accuracy. |
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The official code and pretrained weights are available at the GitHub repository: https://github.com/apning/adaptive-latent-reasoning |
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## Usage |
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All weights used for results in the paper are available on Hugging Face. You can load these models using the function `automodelforcausallm_from_pretrained_latent` from `src.model_creation`. |
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First, set up your environment by cloning the repository and installing dependencies: |
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```bash |
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git clone https://github.com/apning/adaptive-latent-reasoning.git |
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cd adaptive-latent-reasoning |
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conda env create -f environment.yml && conda activate adaptive-latent-reasoning |
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``` |
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Then, you can load a model like this: |
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```python |
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from transformers import AutoTokenizer |
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from src.model_creation import automodelforcausallm_from_pretrained_latent |
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repo_id = "Lapisbird/Llama-adaLR-model-latent-6" # Example model from the paper |
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model = automodelforcausallm_from_pretrained_latent(repo_id) |
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tokenizer = AutoTokenizer.from_pretrained(repo_id) |
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``` |
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For more detailed instructions on replication, training, and evaluation, please refer to the [official GitHub repository](https://github.com/apning/adaptive-latent-reasoning). |
