Pipeline Parallelism is All You Need for Optimized Early-Exit Based Self-Speculative Decoding
Fuente:
arXiv
Saved in:
| Main Authors: | Li, Ruanjun, Liu, Ziheng, Shi, Yuanming, Shao, Jiawei, Zhang, Chi, Li, Xuelong |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding
by: Elhoushi, Mostafa, et al.
Published: (2024)
by: Elhoushi, Mostafa, et al.
Published: (2024)
One Jump Is All You Need: Short-Cutting Transformers for Early Exit Prediction with One Jump to Fit All Exit Levels
by: Seshadri, Amrit Diggavi
Published: (2025)
by: Seshadri, Amrit Diggavi
Published: (2025)
The Diminishing Returns of Early-Exit Decoding in Modern LLMs
by: Wei, Rui, et al.
Published: (2026)
by: Wei, Rui, et al.
Published: (2026)
CAS-Spec: Cascade Adaptive Self-Speculative Decoding for On-the-Fly Lossless Inference Acceleration of LLMs
by: Ning, Zhiyuan, et al.
Published: (2025)
by: Ning, Zhiyuan, et al.
Published: (2025)
PARD-2: Target-Aligned Parallel Draft Model for Dual-Mode Speculative Decoding
by: An, Zihao, et al.
Published: (2026)
by: An, Zihao, et al.
Published: (2026)
Not All Documents Are What You Need for Extracting Instruction Tuning Data
by: Zhang, Chi, et al.
Published: (2025)
by: Zhang, Chi, et al.
Published: (2025)
SpecExit: Accelerating Large Reasoning Model via Speculative Exit
by: Yang, Rubing, et al.
Published: (2025)
by: Yang, Rubing, et al.
Published: (2025)
Training on the Benchmark Is Not All You Need
by: Ni, Shiwen, et al.
Published: (2024)
by: Ni, Shiwen, et al.
Published: (2024)
ScRPO: From Errors to Insights
by: Li, Lianrui, et al.
Published: (2025)
by: Li, Lianrui, et al.
Published: (2025)
Tensor Product Attention Is All You Need
by: Zhang, Yifan, et al.
Published: (2025)
by: Zhang, Yifan, et al.
Published: (2025)
Information Capacity: Evaluating the Efficiency of Large Language Models via Text Compression
by: Yuan, Cheng, et al.
Published: (2025)
by: Yuan, Cheng, et al.
Published: (2025)
Contrast Is All You Need
by: Kilic, Burak, et al.
Published: (2023)
by: Kilic, Burak, et al.
Published: (2023)
Speculative Decoding for Multi-Sample Inference
by: Li, Yiwei, et al.
Published: (2025)
by: Li, Yiwei, et al.
Published: (2025)
Self-Verification is All You Need To Pass The Japanese Bar Examination
by: Shin, Andrew
Published: (2026)
by: Shin, Andrew
Published: (2026)
More Agents Is All You Need
by: Li, Junyou, et al.
Published: (2024)
by: Li, Junyou, et al.
Published: (2024)
Bridging Draft Policy Misalignment: Group Tree Optimization for Speculative Decoding
by: Hu, Shijing, et al.
Published: (2025)
by: Hu, Shijing, et al.
Published: (2025)
Traversal Verification for Speculative Tree Decoding
by: Weng, Yepeng, et al.
Published: (2025)
by: Weng, Yepeng, et al.
Published: (2025)
Gumiho: A Hybrid Architecture to Prioritize Early Tokens in Speculative Decoding
by: Li, Jinze, et al.
Published: (2025)
by: Li, Jinze, et al.
Published: (2025)
Dynamic Early Exit in Reasoning Models
by: Yang, Chenxu, et al.
Published: (2025)
by: Yang, Chenxu, et al.
Published: (2025)
Is Depth All You Need? An Exploration of Iterative Reasoning in LLMs
by: Wu, Zongqian, et al.
Published: (2025)
by: Wu, Zongqian, et al.
Published: (2025)
Rho-1: Not All Tokens Are What You Need
by: Lin, Zhenghao, et al.
Published: (2024)
by: Lin, Zhenghao, et al.
Published: (2024)
Speculative Decoding: Performance or Illusion?
by: Liu, Xiaoxuan, et al.
Published: (2025)
by: Liu, Xiaoxuan, et al.
Published: (2025)
Agents Are All You Need for LLM Unlearning
by: Sanyal, Debdeep, et al.
Published: (2025)
by: Sanyal, Debdeep, et al.
Published: (2025)
Batch Speculative Decoding Done Right
by: Zhang, Ranran Haoran, et al.
Published: (2025)
by: Zhang, Ranran Haoran, et al.
Published: (2025)
Attention Smoothing Is All You Need For Unlearning
by: Zade, Saleh Zare, et al.
Published: (2026)
by: Zade, Saleh Zare, et al.
Published: (2026)
SelfJudge: Faster Speculative Decoding via Self-Supervised Judge Verification
by: Yoon, Kanghoon, et al.
Published: (2025)
by: Yoon, Kanghoon, et al.
Published: (2025)
Decoding Memories: An Efficient Pipeline for Self-Consistency Hallucination Detection
by: Gao, Weizhi, et al.
Published: (2025)
by: Gao, Weizhi, et al.
Published: (2025)
Online Speculative Decoding
by: Liu, Xiaoxuan, et al.
Published: (2023)
by: Liu, Xiaoxuan, et al.
Published: (2023)
Not All Preferences are What You Need for Post-Training: Selective Alignment Strategy for Preference Optimization
by: Dong, Zhijin
Published: (2025)
by: Dong, Zhijin
Published: (2025)
The Disparate Impacts of Speculative Decoding
by: Sandler, Jameson, et al.
Published: (2025)
by: Sandler, Jameson, et al.
Published: (2025)
Constrained Decoding with Speculative Lookaheads
by: Nakshatri, Nishanth, et al.
Published: (2024)
by: Nakshatri, Nishanth, et al.
Published: (2024)
Scaling Laws for Speculative Decoding
by: Yan, Siyuan, et al.
Published: (2025)
by: Yan, Siyuan, et al.
Published: (2025)
Cross-Attention Speculative Decoding
by: Zhong, Wei, et al.
Published: (2025)
by: Zhong, Wei, et al.
Published: (2025)
Mamba Drafters for Speculative Decoding
by: Choi, Daewon, et al.
Published: (2025)
by: Choi, Daewon, et al.
Published: (2025)
RACER: Retrieval-Augmented Contextual Rapid Speculative Decoding
by: Zhang, Zihong, et al.
Published: (2026)
by: Zhang, Zihong, et al.
Published: (2026)
Goose: Anisotropic Speculation Trees for Training-Free Speculative Decoding
by: Jin, Tao, et al.
Published: (2026)
by: Jin, Tao, et al.
Published: (2026)
Self-Speculative Biased Decoding for Faster Re-Translation
by: Zeng, Linxiao, et al.
Published: (2025)
by: Zeng, Linxiao, et al.
Published: (2025)
GRIFFIN: Effective Token Alignment for Faster Speculative Decoding
by: Hu, Shijing, et al.
Published: (2025)
by: Hu, Shijing, et al.
Published: (2025)
Dynamic Depth Decoding: Faster Speculative Decoding for LLMs
by: Brown, Oscar, et al.
Published: (2024)
by: Brown, Oscar, et al.
Published: (2024)
Fast and Cost-effective Speculative Edge-Cloud Decoding with Early Exits
by: Venkatesha, Yeshwanth, et al.
Published: (2025)
by: Venkatesha, Yeshwanth, et al.
Published: (2025)
Similar Items
-
LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding
by: Elhoushi, Mostafa, et al.
Published: (2024) -
One Jump Is All You Need: Short-Cutting Transformers for Early Exit Prediction with One Jump to Fit All Exit Levels
by: Seshadri, Amrit Diggavi
Published: (2025) -
The Diminishing Returns of Early-Exit Decoding in Modern LLMs
by: Wei, Rui, et al.
Published: (2026) -
CAS-Spec: Cascade Adaptive Self-Speculative Decoding for On-the-Fly Lossless Inference Acceleration of LLMs
by: Ning, Zhiyuan, et al.
Published: (2025) -
PARD-2: Target-Aligned Parallel Draft Model for Dual-Mode Speculative Decoding
by: An, Zihao, et al.
Published: (2026)