Deeper is Not Always Better: Mitigating the Alignment Tax via Confident Layer Decoding

The paper introduces a new decoding strategy called Confident Decoding for autoregressive generation in large language models (LLMs), which challenges the assumption that deeper layers always yield better predictions. It employs a dynamic selection process for near-final layers based on entropy-guided search, effectively mitigating alignment perturbations while maintaining performance, as evidenced by improved results on reasoning benchmarks like GPQA-Diamond and Omni-MATH with minimal latency increase. This approach is significant for practitioners as it offers a way to enhance reasoning capabilities in aligned LLMs without additional memory costs.