> For the complete documentation index, see [llms.txt](https://training.continuumlabs.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://training.continuumlabs.ai/training/the-fine-tuning-process/training-processes.md).

# Training Processes

- [Extending the context window](https://training.continuumlabs.ai/training/the-fine-tuning-process/training-processes/extending-the-context-window.md)
- [PyTorch Fully Sharded Data Parallel (FSDP)](https://training.continuumlabs.ai/training/the-fine-tuning-process/training-processes/pytorch-fully-sharded-data-parallel-fsdp.md)
- [Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation](https://training.continuumlabs.ai/training/the-fine-tuning-process/training-processes/train-short-test-long-attention-with-linear-biases-enables-input-length-extrapolation.md): Ofir Press, Noah A. Smith, Mike Lewis
- [YaRN: Efficient Context Window Extension of Large Language Models](https://training.continuumlabs.ai/training/the-fine-tuning-process/training-processes/yarn-efficient-context-window-extension-of-large-language-models.md): Nous Research, EleutherAI, University of Geneva
- [Sliding Window Attention](https://training.continuumlabs.ai/training/the-fine-tuning-process/training-processes/sliding-window-attention.md): Iz Beltagy, Matthew E. Peters, and Arman Cohan
- [LongRoPE](https://training.continuumlabs.ai/training/the-fine-tuning-process/training-processes/longrope.md)
- [Reinforcement Learning](https://training.continuumlabs.ai/training/the-fine-tuning-process/training-processes/reinforcement-learning.md)
- [An introduction to reinforcement learning](https://training.continuumlabs.ai/training/the-fine-tuning-process/training-processes/an-introduction-to-reinforcement-learning.md)
- [Reinforcement Learning from Human Feedback (RLHF)](https://training.continuumlabs.ai/training/the-fine-tuning-process/training-processes/reinforcement-learning-from-human-feedback-rlhf.md): Most often useful when creating domain specific models
- [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://training.continuumlabs.ai/training/the-fine-tuning-process/training-processes/direct-preference-optimization-your-language-model-is-secretly-a-reward-model.md)