That paper describes an experimental diff-focused approach from 2022. It's not clear to me how relevant it is to the way models like Claude 3.7 Sonnet (thinking) and o3-mini work today.
If do not you think past research by OpenAI and Anthropic on how to use LLMs to generate code is relevant to how Anthropic LLMs generate code 3 years later I really don't think it is possible to have a reasonable conversation about this topic with you.
Can we be sure that research became part of their mainline model development process as opposed to being an interesting side-quest?
Are Gemini and DeepSeek and Llama and other strong coding models using the same ideas?
Llama and DeepSeek are at least slightly more open about their training processes so there might be clues in their papers (that's a lot of stuff to crunch through though).
I quoted the paper "Evolution through Large Models" written in collaboration between OpenAI and Anthropic researchers
"In other words, the model learns to predict plausible changes to code from examples of changes made to code by human programmers."
https://arxiv.org/pdf/2206.08896
> The idea that models can only write code if they've seen code that does the exact same thing in the past
How do you get "code that does the exact same thing" from "predicting plausible changes?"