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This experiment is quite interesting, especially since only about 0.016% of the model weights were modified, achieving such a significant performance improvement.

However, I'm rather curious: Does this improvement come from the enhancement of the model's reasoning ability itself, or is it mainly due to the fixed issue of the model repeatedly reasoning and being unable to conclude normally?

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Mainly due to the looping.

Before the edit, 93% of MATH-500 answers hit the 32k token limit, compared to 33% after.

With 10 and 25 forward passes, looping barely decreased (87% and 86% still hit the limit) and the score didn't change much. The score jump only happened at 50 forward passes when looping decreased.

Astra edited the weights to bring out a capability the model already had rather than enhancing the ability itself. Pretty impressive.

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