I don't think the concept of natural language reasoning tokens is here to stay

Current open source LLMs have an emergent structure where the first couple layers transform the input into some internal representation. Then the middle does multiple reasoning steps, and the end transforms it back to one output token. You can repeat the middle to get a stronger, slower model, so it is abstract reasoning. And Anthropic had a blog post about actually looking at what the middle does and mapping it back to concept for inspection

With that in mind, what's the point of reasoning tokens? They throw rich internal state away, for the benefit of inspectability and variable-length reasoning. But now that we understand the internal structure better we can dynamically vary how many loops the model spins through those middle layers, and still get enough insight into what's happening by decoding internal state. This takes some reengineering of model architecture and inference engines (especially to still get good batch performance), but the potential benefits seem to big to ignore