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If you look at say https://arxiv.org/pdf/2502.05171 Figure 8, it does not appear that increasing recurrent depth in deployment is a useful "test-time scaling" axis: which is to say, when trained at a max depth of 32, setting depth to 64 at inference time bought them no improvement.
Thought who knows how RL would change the story, or if their model in practice has similar characteristics to the research products visible from the last few years.