AI

DLoop: Looped Speculative Decoding

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Briefings

DLoop improves speculative decoding speed by 5 to 41 percent while maintaining lossless decoding. The method adapts multiple drafting stages before verification, reducing target-model forward passes. It applies to diverse methods including EAGLE-3, DFlash, Domino, DSpark, and multi-token prediction modules. Code is available at https://github.com/naver-ai/DLoop.

Key points

• DLoop improves speculative decoding speed by 5 to 41 percent while maintaining lossless decoding.
• Loop-aware training keeps the draft model reliable in additional drafting stages by exposing it to its own hidden states for unverified draft tokens.

Context

Speculative decoding accelerates autoregressive generation in large language models by using lightweight draft models to propose tokens that target models verify. DLoop adapts multiple drafting stages before verification, reducing target-model forward passes. Code is available at https://github.com/naver-ai/DLoop.

Limitations

Evidence covers diverse speculative decoding methods including EAGLE-3, DFlash, Domino, DSpark, and multi-token prediction modules. Wall-clock speedup varies between 5 to 41 percent across methods. Code availability is confirmed but specific performance metrics for each method are not detailed.

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