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[RFC] Improving Ray for Post-Training / RL for LLM Projects #54021

@richardliaw

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@richardliaw

Over the past year, many projects have been launched leveraging Ray to scale out post-training and RL for LLMs. From our perspective, we’d like to ensure that Ray continues to be a great fit for these use cases and address any bugs or usability gaps in Ray.

We've spoken to a variety of project creators over the last couple of weeks and have gotten great feedback.

Below is our currently identified list of issues and features that we plan to address, but we’d also be eager to hear if there is any other feedback as well.

List of issues to address:

Core specific issues:

Open Questions

  • Anything we should do to better improve SLURM support?

Key Projects

We welcome folks to participate, and please feel free to let us know if there are other items to address.

cc @robertnishihara @SumanthRH @erictang000 @kouroshHakha @kevin85421 @stephanie-wang

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RFCRFC issuescoreIssues that should be addressed in Ray CorellmobservabilityIssues related to the Ray Dashboard, Logging, Metrics, Tracing, and/or Profilingperformanceusability

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