rlxp-plan-parallel-batch

Planning parallel reinforcement learning experiments requires ranking validated candidates, assigning seeds, and allocating GPUs according to hardware constraints and remaining budget. This process prevents resource overcommitment and execution conflicts across concurrent runs.

To prevent configuration collisions during parallel RL studies, apply an isolation strategy such as using separate worktrees. This ensures that concurrent code or configuration changes do not interfere with each other during execution.

The best way to manage seed assignment for batch reinforcement learning runs is to use a batch planner that distributes seeds across validated candidates. This ensures multi-seed validation and incumbent checks are properly phased within the remaining budget.

Yes, you can define a promotion flow for reinforcement learning candidates that includes smoke runs, short trials, and multi-seed validation. This flow is structured based on your remaining budget and parallelism limits to ensure safe candidate evaluation.

Yes, validated candidates are required before planning a parallel RLXP batch run. The planning process needs these candidates alongside hardware constraints, remaining budget, incumbent context, and parallelism limits to produce a safe batch plan.

Limitations in orchestrating large-scale parallel reinforcement learning tasks include hardware constraints, remaining budget limits, and parallelism caps. These factors restrict the number of concurrent candidates and dictate the queue phasing required for safe execution.