Where Draft Trees Lose Target Mass: Exit-Guided Speculative Decoding
cs.AI
Submitted: 2026-10-08
Updated: 2026-10-08
Code: https://github.com/hsj576/TEV
Terminology
Sources
- Accelerating Large Language Model Decoding with Speculative Sampling
- Evaluating Large Language Models Trained on Code
- Recurrent Drafter for Fast Speculative Decoding in Large Language Models
- Training Verifiers to Solve Math Word Problems
- The Llama 3 Herd of Models
- Verification-Aware Training for Speculative Decoding
- Draft-OPD: On-Policy Distillation for Speculative Draft Models
- DARTree: Speculative Diffusion Decoding with Autoregressive Draft Trees
- Accelerating Speculative Decoding with Block Diffusion Draft Trees
- The Perfect Blend: Redefining RLHF with Mixture of Judges
- Qwen3 Technical Report
Related papers
- MAVEN-T: Reinforced Heterogeneous Distillation for Real-Time Multi-Agent Trajectory Prediction
- Model Discovery Agent: LLM-assisted Bayesian experiment design for data-efficient discovery of mechanistic world models
- Agents' Last Exam
- The Clinician's Veto: Navigating Trust, Liability, and Uncertainty in Autonomous AI Prescribing
- MindHelper: Closed-Loop Embodied Mental-State Reasoning for Precision Intervention
- Incumbent Advantage: Brand Bias and Cognitive Manipulation Dynamics in LLM Recommendation Systems