Large Flow Language Models
The sequential bottleneck of sampling one token at a time Large language models can compute an entire sentence in parallel during training, but when generating text they…
Speculative-Decoding
10 pages
DLite: History-free Speculative Decoding for Agentic AI
DLite explained through a candidate-verification example, cache ownership, and a careful comparison of acceptance length and decoding speed.
Trajectory-Level Speculation with Semantic Verification for Tool-Using Agents
An analysis of SpecAgent, where a weaker model drafts tool calls and a stronger model verifies them, covering its design, experimental results, and limitations.
Contextual Correction of Parallel Drafts for Speculative Decoding
DCOCO reselects existing parallel draft candidates using contextual scores. This review distinguishes accepted length from actual throughput and examines correction…
GDN Tree-Scan: Served Tree Verification for Recurrent-Hybrid Language Models
LumoTree: Path-Parallel Speculative Verification for Hybrid Language Models TL;DR — Tree speculative decoding in hybrid models that mix Gated DeltaNet with full attention…
Adapting Tree-Structured Speculative Decoding to DeepSeek-V4 for Efficient Inference
Branching on Compressed Memory: Adding Tree Reasoning to DeepSeek-V4 TL;DR Adding tree-structured speculative decoding to DeepSeek-V4-Flash yields longer acceptance…
H-Spec: Parallel Speculative Decoding Without a Drafter-Side KV Cache
Parallel Inference without Drafter-Side KV Cache: H-Spec's Hybrid Speculative Decoding TL;DR shows that the bottleneck in concurrent serving is that a block-diffusion…
How Lossless Is Lossless Speculative Decoding? The Role of Numerical Precision in Orthrus
Is "Lossless" Speculative Decoding Really Lossless? — The Numerical-Precision Trap Exposed by Reproducing Orthrus TL;DR — Orthrus attaches a diffusion view to a frozen AR…
Online Draft Co-Training for Speculative Decoding in Large-Scale, Long-Context RL Post-Training
Two Designs That Make Speculative Decoding Practical for Large-Scale, Long-Context RL Post-Training TL;DR : Rollout generation dominates the wall-clock time of RL…
Unlocking Lossless Speedups in LLMs via Discrete Diffusion
Uno: AR and Diffusion in One Model — Lossless Parallel Generation for LLM Acceleration TL;DR — LLMs are slow because next-token prediction (NTP) can emit only one token…