[Docs] Improve documentation for RLHF example

[crypdick]

Purpose

Improve documentation for RLHF example
(Decomposing #20040 into multiple PRs)

[github-actions]

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[gemini-code-assist]

Summary of Changes

Hello @crypdick, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

I have reviewed this pull request, which focuses entirely on enhancing the documentation within the examples/offline_inference/rlhf.py file. The primary goal is to provide a clearer and more comprehensive understanding of the RLHF example's architecture, its operational steps, and the underlying Ray and GPU resource management, making the code more accessible and understandable for users.

Highlights

• Improved Module Docstring: The main docstring of rlhf.py has been significantly expanded and rewritten to offer a detailed explanation of the RLHF demonstration, including its use of vLLM and Ray, GPU allocation, and a step-by-step breakdown of the example's execution flow.
• Enhanced Code Comments: Numerous inline comments throughout the file have been updated for improved clarity, conciseness, and informational value. This includes better explanations for the MyLLM class, Ray placement group setup, vLLM engine initialization, and the simulated training and weight synchronization processes.
• Clarified GPU and Ray Integration: The documentation now explicitly details how GPUs are utilized (GPU 0 for training, GPUs 1-2 for vLLM inference) and how Ray is leveraged for process placement and inter-process communication, providing crucial context for understanding the example's distributed nature.

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[gemini-code-assist]

Code Review

This pull request significantly improves the documentation for the RLHF example by rewriting the main docstring and clarifying comments throughout the script. The changes make the example easier to understand.

[hmellor]

LGTM just one nit about MD formatting