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The good stuff:

1. The first step is to have the model reason about the problem. They describe it using bullet points and focus on the goal, inputs, outputs, rules, constraints, and any other relevant details.2. Then, they make the model reason about the public tests and come up with an explanation of why the input leads to that particular output.3. The model generates two to three potential solutions in text and ranks them in terms of correctness, simplicity, and robustness.4. Then, it generates more diverse tests for the problem, covering cases not part of the original public tests.5. Iteratively, pick a solution, generate the code, and run it on a few test cases. If the tests fail, improve the code and repeat the process until the code passes every test.

The bad stuff:

The weird stuff:
 
IMO very important paper. They trained a relatively small LLM to only get a chess position as input and output the strongest move. So a very challenging dataset. The AI got a 2900 blitz rating just on this. No search. The conclusion is that we may be underestimating how incredibly intelligent these LLMs are if we give them good datasets. Which bodes very well for Optimus, FSD etc…