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arXiv · 2610.29527

RCSD: Reinforcement and Continual Learning for Domain-Specific Speculative Decoding

Mingkang Liu·Weiming Meng·Peipei Jiao·Jiawei Zhang·Tianyuan Liu·Junyan Li·Lichen Pang
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Abstract

Speculative decoding accelerates large language model inference by employing a compact draft model to propose candidate tokens that are verified in parallel by a target model. Conventional training relies on soft-label distillation, which forces the draft model to mimic the target's full output distribution, including high-entropy regions that degrade acceptance rates and increase early rejection risk. We propose RCSD (Reinforcement and Continual Learning for Speculative Decoding), a training framework that aligns draft model optimization with the system-level objective of maximizing acceptance length. RCSD leverages naturally occurring accepted and rejected sequences during speculative inference to construct implicit preference pairs, with masked loss to avoid penalizing target-aligned tokens. Evaluated across multiple benchmarks, RCSD improves average acceptance length by 35% over HASS and achieves 1.35x faster end-to-end inference than HASS on practical workloads, without compromising generation quality.

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Mingkang Liu, Weiming Meng, Peipei Jiao, Jiawei Zhang, Tianyuan Liu, Junyan Li, Lichen Pang. 2026-10-21. RCSD: Reinforcement and Continual Learning for Domain-Specific Speculative Decoding. https://arxiv.org/abs/2610.29527

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