What are the infrastructure bottlenecks when training a...

Training a reasoning model with RL faces three primary infrastructure bottlenecks. First, rollout generation blocks training; this is mitigated by separating inference and training worker pools with asynchronous message passing. Second, variable CoT lengths lead to uneven batches and GPU underutilization, which can be addressed through sequence packing or per-length-bucket batching. Third, syncing updated weights from the training process to the inference server is complex, often requiring experimental APIs or restarting the inference engine each iteration.