CORAL improves speculative decoding by solving the representation inconsistency problem between draft models and target LLMs, enabling faster inference with lighter models.

• Creates consistent representations across multi-step training phases
• Implements novel weight-grouping mechanism to improve alignment
• Achieves acceleration of 1.7-2.0x compared to baseline techniques
• Enables effective use of smaller draft models while maintaining quality

This engineering breakthrough directly addresses computational efficiency bottlenecks in LLM inference, making deployment more cost-effective while preserving output quality.

CORAL: Learning Consistent Representations across Multi-step Training with Lighter Speculative Drafter