Hugging Face releases multi-harness RL training guide

On October 3, 2026, Hugging Face's post-training team published a guide on reinforcement learning training across multiple coding harnesses. The announcement came from Lewis, a member of the team, who shared it with the LocalLLaMA community on Reddit.

In the post, Lewis said the team had been exploring how to train open models inside different coding harnesses, and had written a long guide describing how they solved the problem with open source libraries: TRL, and the Harbor framework for RL environments. According to the post, the goal is to get the best possible performance out of open models on each custom harness — harnesses are treated as their own setups rather than as interchangeable ones.

Lewis framed the write-up as broadly relevant, noting that everyone nowadays has their own custom harness; the example he gave is Pi plus extensions. He said the team hopes readers find the guide interesting.

That is where things stand: the guide has been published and shared publicly, and no further steps are described in the available material. The report does not cover the guide's individual sections, whether example code or configs accompany it, which models were used in the experiments, what numbers or benchmark results were reported, or any follow-up plans from the post-training team. Beyond Lewis's description of the guide as a long one, its length is not specified, and no coding harnesses other than the Pi example are named. TRL and Harbor are both described as open source, but the post does not detail how they are configured.