M2R2: Mixture of Multi-Rate Residuals for Efficient Transformer Inference Article Swipe
Nikhil Bhendawade
,
Mahyar Najibi
,
Devang Naik
,
I. I. Belousova
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2502.02040
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2502.02040
Residual transformations enhance the representational depth and expressive power of large language models (LLMs). However, applying static residual transformations across all tokens in auto-regressive generation leads to a suboptimal trade-off between inference efficiency and generation fidelity. Existing methods, including Early Exiting, Skip Decoding, and Mixture-of-Depth address this by modulating the residual transformation based on token-level complexity. Nevertheless, these approaches predominantly consider the distance traversed by tokens through the model layers, neglecting the underlying velocity of residual evolution. We introduce Mixture of Multi-rate Residuals (M2R2), a framework that dynamically modulates residual velocity to improve early alignment, enhancing inference efficiency. Evaluations on reasoning oriented tasks such as Koala, Self-Instruct, WizardLM, and MT-Bench show M2R2 surpasses state-of-the-art distance-based strategies, balancing generation quality and speedup. In self-speculative decoding setup, M2R2 achieves up to 2.8x speedups on MT-Bench, outperforming methods like 2-model speculative decoding, Medusa, LookAhead Decoding, and DEED. In Mixture-of-Experts (MoE) architectures, integrating early residual alignment with ahead-of-time expert loading into high-bandwidth memory (HBM) accelerates decoding, reduces expert-switching bottlenecks, and achieves a 2.9x speedup, making it highly effective in resource-constrained environments.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2502.02040
- https://arxiv.org/pdf/2502.02040
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4407185598
All OpenAlex metadata
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https://openalex.org/W4407185598Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2502.02040Digital Object Identifier
- Title
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M2R2: Mixture of Multi-Rate Residuals for Efficient Transformer InferenceWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-02-04Full publication date if available
- Authors
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Nikhil Bhendawade, Mahyar Najibi, Devang Naik, I. I. BelousovaList of authors in order
- Landing page
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https://arxiv.org/abs/2502.02040Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2502.02040Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2502.02040Direct OA link when available
- Concepts
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Inference, Transformer, Econometrics, Computer science, Mathematics, Artificial intelligence, Engineering, Electrical engineering, VoltageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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