Will the noise_step method hold up in 6 months?

A new method for training neural networks, called noise_step, has gained traction on Twitter. The core innovation is that it purportedly allows neural networks to be trained using 1.58-bit precision instead of the standard f16 precision.

You can explore more about noise_step through the following links:

• Repo: https://github.com/wbrickner/noise_step

• Twitter announcement: https://x.com/_brickner/status/1871348156786704657

This market resolves YES if, within six months:

• A model with at least 100 million parameters is successfully trained using noise_step at 1.58-bit precision,

• With verifiable results that align with the original claims (e.g., comparable or better performance than f16-based training).

The market resolves NO if:

• The method is debunked,

• Demonstrated to be ineffective,

• Or if no verifiable evidence of its success on a model of the specified size emerges within the time frame.