Deeper is Not Always Better: Mitigating the Alignment Tax via Confident Layer Decoding
Confident Decoding addresses the 'Alignment Tax,' where late-stage model adjustments ruin complex reasoning. It uses a training-free method to dynamically select a reliable near-final layer during decoding by searching for an entropy trough backward through the layers. This bypasses final layer biases to preserve accurate, complex logic.