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Miles

Miles is an open-source reinforcement learning framework for large-scale LLM post-training, pairing SGLang rollout with Megatron-LM training at trillion-parameter scale.

Their llms.txt

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# Miles

> Miles is an open-source reinforcement learning framework for large-scale LLM post-training, pairing SGLang rollout with Megatron-LM training at trillion-parameter scale.

- [Welcome](https://miles.radixark.com/docs/index.md): Miles is an open-source RL framework for large-scale LLM post-training, pairing SGLang rollout with Megatron-LM training at trillion-parameter scale.
- [Getting Started](https://miles.radixark.com/docs/getting-started/index.md): Install Miles and run your first RL training job — the two pages you need to go from zero to a working loop.
- [Installation](https://miles.radixark.com/docs/getting-started/installation.md): Install Miles on NVIDIA or AMD GPUs. Docker is the recommended path.
- [Quick Start](https://miles.radixark.com/docs/getting-started/quick-start.md): Get an RL training job up and running in under an hour.
- [User Guide](https://miles.radixark.com/docs/user-guide/index.md): Concepts, launch scripts, customization hooks, and a complete CLI reference.
- [Core Concepts](https://miles.radixark.com/docs/user-guide/concepts.md): The four objects that make up every Miles job, and how data flows between them.
- [Launch Script](https://miles.radixark.com/docs/user-guide/launch-script.md): What a Miles launch script does when you run it, how it is structured, and the three ways to override a recipe.
- [Argument Groups](https://miles.radixark.com/docs/user-guide/argument-groups.md): The launch-script argument groups used by Miles recipes, with links to the flags that belong in each group.
- [Fully Async RL](https://miles.radixark.com/docs/user-guide/fully-async.md): How fully async rollout decouples generation from training, which flags control it, and how to evaluate without stalling the trainer.
- [Training Backends](https://miles.radixark.com/docs/user-guide/training-backend.md): The contract Megatron-LM, FSDP and torchtitan all implement, which one to pick, and how to configure parallelism, GPU layout, offload, and checkpoints for each.
- [Monitoring & Logging](https://miles.radixark.com/docs/user-guide/monitoring.md): wandb, structured logs, profiling, and what to look at when something looks off.
- [Customization](https://miles.radixark.com/docs/user-guide/customization.md): The plug-points where you can drop in your own Python without forking Miles.
- [Generate Endpoint](https://miles.radixark.com/docs/user-guide/generate-endpoint.md): Write a custom generate function against SGLang's raw /generate endpoint, owning prompt construction, tokens, and loss masks.
- [Agentic Rollout (TITO)](https://miles.radixark.com/docs/user-guide/agentic-rollout.md): Configure an OpenAI-compatible agent loop with Token-In-Token-Out trajectory assembly.
- [Agentic Environments](https://miles.radixark.com/docs/user-guide/environments.md): How Miles trains on RL environments — datasets with rewards, self-wired environments, and optional external ecosystems.
- [Sandbox Providers](https://miles.radixark.com/docs/user-guide/sandb
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