Confident Layer Decoding Skips Final LLM Layers to Reduce Alignment Tax

Confident Decoding is a training-free method that uses entropy-guided backward search to select an intermediate near-final layer for token generation instead of always using the last layer. The paper identifies a Guess-Refine-Perturb pattern where final layers can push predictions toward generic or alignment-preferred tokens, degrading output quality.