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Training & Fine-tuningHard· IN2 training· DIFF Transformer· attention mechanisms
Compare IN2 training to DIFF Transformer as fixes for l...
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See plansThe mechanistic difference lies in where the fix is applied: IN2 is a data-level intervention that forces the model to learn position-agnostic representations, whereas DIFF Transformer is an architectural change that cancels shared positional noise within the attention heads. Because they operate on different layers of the model stack—data distribution versus computational logic—they are not interchangeable and can be deployed simultaneously to improve long-context performance.