> ## Documentation Index
> Fetch the complete documentation index at: https://docs.wandb.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# W&B Training

> Post-train your models using reinforcement learning and supervised fine-tuning

Now in public preview, W\&B Training offers serverless post-training for large language models (LLMs), including both reinforcement learning (RL) and supervised fine-tuning (SFT).

* **[Serverless RL](/training/serverless-rl)**: Improve model reliability performing multi-turn, agentic tasks while increasing speed and reducing costs. RL is a training technique where models learn to improve their behavior through feedback on their outputs.
* **[Serverless SFT](/training/sft-training)**: Fine-tune models using curated datasets for distillation, teaching output style and format, or warming up before RL.

W\&B Training includes integration with:

* [ART](https://art.openpipe.ai/getting-started/about), a flexible fine-tuning framework.
* [RULER](https://openpipe.ai/blog/ruler), a universal verifier.
* A fully-managed backend on [CoreWeave Cloud](https://docs.coreweave.com/docs/platform).

To get started, satisfy the [prerequisites](/training/prerequisites) to start using the service and then see the [Serverless RL quickstart](https://art.openpipe.ai/getting-started/quick-start) or the [Serverless SFT docs](https://art.openpipe.ai/fundamentals/sft-training) to learn how to post-train your models.