Tag: gpt-oss

This is the runtime stack link for the gpt-oss moment: kernels from the Hub, MXFP4, tensor and expert parallelism, dynamic KV cache behavior, continuous batching, and faster loading.

Worth keeping because it connects model release excitement to the boring but decisive parts of deployment: memory, cache shape, batching, and what hardware the trick actually runs on.

This is a docs link rather than an essay, but it belongs in the stream because it gives a practical path for experimenting with gpt-oss reinforcement learning workflows.

The caution is that a runnable recipe is not the same as a product-ready alignment loop. Keep evals, data provenance, and safety gates in front of the training command.