When making such claims it's generally good form to give reproducible examples of why you think it must be so. Otherwise it leaves everyone else stabbing in the dark at trying to follow your thought process before being able to weigh if the version they land on is convincing to them. E.g. the same for a counterclaim:

o1-preview doesn't seem to have the ability to execute python code (similar to it's other lackings, such as not being able to call whisper/dall-e integrations). Example script:

import gzip
import base64
from io import BytesIO

encoded_string = b'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'
compressed_string = base64.b64decode(encoded_string)
with gzip.GzipFile(fileobj=BytesIO(compressed_string)) as gzip_file:
original_string = gzip_file.read().decode('utf-8') # Assuming the original string is in UTF-8 encoding

print(original_string)

This outputs ascii art of a TI-86 calculator. Given the prompts:

> What does the ascii art output of this script represent: ${script}

and

> Execute ${script} and guess what the output represents.

and

> Execute ${script}

o1-preview produces complete gibberish from guessing random patterns in numbers, giving sample output of random ASCII art lines, and saying things in its train of thought like "I copied and pasted the code into a Python interpreter. It generated ASCII art, resembling an elephant with features like ears, trunks, and texture patterns". Despite it saying it ran these in an interpreter it seems to be simulating that despite knowing it's the ideal step as plain ol' 4o just outputs:

> The output of the Python code is an ASCII art representation of a Texas Instruments TI-86 calculator. Here is the ASCII art: ${exact_ti-86_ascii_art}

.

Apart from that hole in the claim, simply expanding the thought process of prompts poking at this:

> Explain how to find the number of occurrences of a letter in a word in general then use what you find to count the number of 'c's in 'occurrences'. Don't use programming or environment execution, particularly not python, use a natural process a person could understand in natural language.

Takes only 6 seconds, has a thought train which does not include anything about code, follows the exact method it created step by step in text, and correctly outputs there are 3 'c's in occurrences.

.

Apart from the second hole in the claim, using additional inference tokens to come up with a chain of thought which would involve coming up with code to use, deploying it, and using the result would indeed still be an example of the new model using inference compute time to improving its quality rather than adding specific questions to the training.