ResiSpec: Enhancing Multi-Candidate Speculative Sampling via Residual Distribution Shaping

arXiv:2608.24411 · cs.AI · Submitted 2026-08-25 · Read on arXiv

cs.AI

Submitted: 2026-08-25

Updated: 2026-08-25

Code: https://github.com/Czzzk/Resispec

Terminology

• Verification — 57× in this paper · explained in 2 episodes
• Speculative Decoding — 28× in this paper · explained in 2 episodes
• Alignment — 8× in this paper · explained in 1 episode
• Large Language Model — 6× in this paper
• Transformer — 5× in this paper · explained in 2 episodes
• Core — 4× in this paper · explained in 2 episodes
• Ablation — 3× in this paper · explained in 1 episode
• Benchmark — 3× in this paper · explained in 2 episodes
• Fidelity — 3× in this paper · explained in 2 episodes
• Ablation study — 1× in this paper · explained in 1 episode
• Algorithmic Bias — 1× in this paper · explained in 1 episode
• Allocator — 1× in this paper · explained in 1 episode
• Axioms — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Degeneracy — 1× in this paper · explained in 3 episodes
• Generalizability — 1× in this paper · explained in 6 episodes
• Generative Model — 1× in this paper · explained in 2 episodes
• Generative Models — 1× in this paper · explained in 2 episodes
• Instruction Tuning — 1× in this paper · explained in 2 episodes
• Kullback-Leibler (KL) Divergence — 1× in this paper · explained in 3 episodes
• Large Language Model (LLM) — 1× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• Robustness — 1× in this paper · explained in 5 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes

Sources

• GPT-4 Technical Report
• Hydra: Sequentially-Dependent Draft Heads for Medusa Decoding
• Adaptive Input Representations for Neural Language Modeling
• Controlling Computation versus Quality for Neural Sequence Models
• Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads
• Accelerating Large Language Model Decoding with Speculative Sampling
• Sequoia: Scalable, Robust, and Hardware-aware Speculative Decoding
• Break the Sequential Dependency of LLM Inference Using Lookahead Decoding
• Speculative Decoding and Beyond: An In-Depth Survey of Techniques
• Towards Optimal Multi-draft Speculative Decoding
• SpecDec++: Boosting Speculative Decoding via Adaptive Candidate Lengths
• EAGLE-2: Faster Inference of Language Models with Dynamic Draft Trees
• Language Models are Few-Shot Learners
• Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM
• Instruction Tuning with GPT-4
• Efficiently Scaling Transformer Inference
• Accelerating LLM Inference with Staged Speculative Decoding
• LLaMA: Open and Efficient Foundation Language Models
• Llama 2: Open Foundation and Fine-Tuned Chat Models
• Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding

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