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Decoupled Contrastive Decoding via Expert-Aligned Drafting

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Do you know Zhixuan Liu?You can claim authorship or link another user.Do you know Zhichen Dong?You can claim authorship or link another user.Do you know Yuanfu Wang?You can claim authorship or link another user.Do you know Chao Yang?You can claim authorship or link another user.

Abstract

Contrastive Decoding (CD) improves generation quality, but its amateur-model pass makes decoding expensive. Accelerating CD with speculative decoding raises a proposal-alignment question: should the contrastive signal shape the drafter, or should it remain only in verification? We study this question in the lightweight feature-level drafter regime. Two controlled diagnostics, matched Cross-alpha training and an Approximate Dual-Drafter decomposition, give the same diagnosis: contrastive-aware drafting does not consistently improve over expert-aligned drafting because the contrastive correction is usually weaker than drafter error, and reconstruction can amplify that error. We introduce Decoupled Contrastive Decoding (DCD), which drafts with an expert-aligned lightweight proposer and applies the amateur only in unchanged CD verification. Standard speculative verification preserves the vanilla-CD output distribution. Across the main 8B settings, EAGLE3-based DCD achieves average greedy speedups of 1.65 to 1.95x over vanilla CD and reduces MMLU proposal-path latency by about 5 to 12x relative to amateur-coupled proposal paths.

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Publication notes

Author note
28 pages, 11 figures, 20 tables. Code: https://github.com/chadlzx/dcd