VIA-SD: New Multi-Tier Framework Improves Efficiency of Speculative Decoding for Large Language Models
Researchers have developed VIA-SD, a new speculative decoding method that uses a hierarchical verification system with a lightweight intermediate verifier to reduce computational costs in large language model inference. The approach processes draft tokens at three confidence levels—direct acceptance, slim-verifier regeneration, and full-model verification—rather than using binary accept/reject decisions. The method achieves 10-20% speedups over existing speculative decoding approaches while reducing rejection rates by 0.10-0.22 across multiple tasks and model families.
VIA-SD introduces a multi-tier verification framework for speculative decoding, a technique that addresses the high inference costs of large language models by using lightweight drafters to generate token candidates that are then validated in parallel. The key innovation is the use of a slim-verifier—a submodel derived from the full verifier through intra-model routing—to handle tokens requiring moderate verification resources, reducing expensive full-model calls. The hierarchical approach routes tokens to three tiers based on confidence: high-confidence tokens are directly accepted, medium-confidence tokens are processed by the slim-verifier, and uncertain tokens receive full-model verification. Across four representative tasks and multiple model families, VIA-SD reduces rejection rates by 0.10-0.22 and delivers 10-20% speedups over strong speculative decoding baselines, while achieving 2.5-3x acceleration compared to non-drafting decoding. The framework is compatible with existing speculative decoding systems without requiring modifications to their training procedures, suggesting multi-tier speculative decoding as a general paradigm for scalable and efficient LLM inference.
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- arXiv cs.AICenter
VIA-SD: Verification via Intra-Model Routing for Speculative Decoding
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