Same here, it's one thing if I'm just screwing around, but if I'm trying to do anything serious, I need to at least have a handle on what it's doing, and, when thinking traces are available, keeping track of any logical errors in the model's reasoning.
>"illegible reasoning in a few reinforcement-learning environments over long rollout"
Yet, I get the point that you're making: those tokens essentially are an internal scratchpad for the LLM which isn't required to logically lead to the output.
Still not meaningful -- https://arxiv.org/pdf/2504.09762; even for local models, the reasoning traces are often filtered and summarized to sound sensible to humans. And even if not, they don't necessarily represent what the model is thinking.
