M2R2 introduces a novel approach that dynamically applies residual transformations at different rates across tokens during LLM inference, significantly improving efficiency.

• Achieves up to 1.9x speedup with minimal quality degradation
• Implements token-level adaptivity by learning when to apply full vs. lightweight processing
• Requires no model retraining and can be applied across various model architectures
• Outperforms existing methods like Early Exiting and Skip Decoding

This research matters for engineering teams seeking to optimize LLM deployment in resource-constrained environments, reducing computational costs while maintaining generation quality.

M2R2: Mixture of Multi-Rate Residuals for Efficient Transformer Inference