does the whole out loop actually need that like this kind of shape. So let's dive into this training is one tightly coupled job. Every step has collectives all reduced and the model parallel communication that part actually wants one fast fabric a RDM connected rollout is a fleet of serving jobs. It generates trajectories call environments or maybe tools and they will be sending back data back to the trainer. So cross rollout jobs there's no global or reduce. So the thing I want to move here is not back propagation. Back propagation should stay in the cluster. The run the rollout fleet is the one that can leave. More precisely, the movable unit is the rollout serving island a coherent endpoint or maybe a local group of endpoint or that they can be serving one policy version inside island. A large model may still be having like local parallelism. They can do PD segregation. They can have like local serving constraints. So across islands the dependency is much lighter right now. policy version in and the trajectory and the metadata out. So once we define the unit that way the architecture is much much more natural. Once we define the movable unit the architecture is very straightforward. In this case we just have trainer standing audic cluster and that's where the back proper and the collective go the rollout side will be fing out across the SPR. Each rollout island will be can be single engine will be a local serving group depending on the model and the serving topology there. across island there is no global or reduce that's the most important thing there the global interface is very simple the trainer send policy weight version out and the rollout sending trajectory and the metadata back at this