GRIFFIN is a novel framework that significantly improves speculative decoding efficiency in LLMs through better token alignment between training and inference phases.

Key Innovations:

• Token-alignable training strategy using loss masking for better prediction accuracy
• Token-alignable draft model design that minimizes misalignment issues
• Enhanced speculative decoding resulting in faster inference without sacrificing output quality
• Engineering breakthrough that addresses a fundamental computational bottleneck in LLM performance

This research matters because it tackles a critical limitation in current acceleration methods, potentially enabling more efficient deployment of large language models in production environments.

GRIFFIN: Effective Token Alignment for Faster Speculative Decoding