| base_model: meta-llama/Llama-3.2-1B-Instruct | |
| datasets: | |
| - whynlp/gsm8k-aug | |
| library_name: transformers | |
| license: llama3.2 | |
| pipeline_tag: text-generation | |
| tags: [] | |
| # Learning When to Stop: Adaptive Latent Reasoning via Reinforcement Learning | |
| This repository hosts a model presented in the paper "[Learning When to Stop: Adaptive Latent Reasoning via Reinforcement Learning](https://huggingface.co/papers/2511.21581)". | |
| Latent reasoning is a novel development in Transformer language models that compresses reasoning lengths by directly passing information-rich previous final latent states. This model implements an adaptive-length latent reasoning approach optimized via a post-SFT reinforcement-learning methodology. This optimization minimizes reasoning length while maintaining accuracy, demonstrating a 52% drop in total reasoning length with no penalty to accuracy on the Llama 3.2 1B model and the GSM8K-Aug dataset. | |
| - **Paper**: [Learning When to Stop: Adaptive Latent Reasoning via Reinforcement Learning](https://huggingface.co/papers/2511.21581) | |
| - **Code**: [https://github.com/apning/adaptive-latent-reasoning](https://github.com/apning/adaptive-latent-reasoning) | |
| ## Sample Usage | |
| You can load this model and other trained weights using the `automodelforcausallm_from_pretrained_latent` function from `src.model_creation`, as demonstrated in the official GitHub repository: | |
| ```python | |
| from transformers import AutoTokenizer | |
| # For full functionality, clone the official GitHub repo: https://github.com/apning/adaptive-latent-reasoning | |
| # and ensure 'src.model_creation' is in your Python path or adapt the import. | |
| from src.model_creation import automodelforcausallm_from_pretrained_latent | |
| repo_id = "Lapisbird/Llama-adaLR-model-latent-6" # Example repo_id from the paper's GitHub README | |
| model = automodelforcausallm_from_pretrained_latent(repo_id) | |
| tokenizer = AutoTokenizer.from_pretrained(repo_id) | |
| print(f"Model '{repo_id}' and tokenizer loaded successfully.") | |
| # You can now use 'model' and 'tokenizer' for inference as described in the paper. | |
| ``` |