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#speculative-decoding

4 curated events
papersTODAY 04:00 UTC

AdaFlash: Adaptive Speculative Decoding with On-Policy Distilled Diffusion Drafters

A new arXiv paper proposes AdaFlash, a speculative decoding method that uses diffusion-based draft models distilled on-policy to speed up large language model inference. The approach adapts the drafting process rather than relying on a fixed draft model, aiming to improve acceptance rates during verification by the target model. It builds on prior work in this line, including DFlash, and appears as a revised submission.

speculative-decoding/diffusion/inference/research
papersTODAY 04:00 UTC

Study examines numerical precision limits in Orthrus lossless speculative decoding

A new arXiv paper investigates whether speculative decoding with the Orthrus architecture remains truly lossless when numerical precision is taken into account. Orthrus is a hybrid autoregressive-diffusion system that drafts several tokens at once and verifies them with a frozen autoregressive model, and its claimed exactness depends on the draft and verification steps producing identical results. The work analyzes how floating-point rounding in these computations can break that equivalence in practice.

speculative-decoding/research/inference/numerical-precision
papersTODAY 04:00 UTC

Paper Proposes Reusing Rejected Tokens to Speed Up Speculative Decoding

A new arXiv paper introduces a technique called carryover drafting that reuses the model states computed for rejected draft tokens during speculative decoding. Because verification already produces representations for both accepted and rejected tokens, the method puts the discarded ones to further use instead of throwing them away. The goal is to improve the efficiency of LLM inference, where speculative decoding lets one target forward pass accept multiple drafted tokens at once.

speculative-decoding/llm-inference/efficiency/arxiv
papersSEP 10 04:00 UTC

Osprey: Target-Agnostic Pre-training Builds Stronger Draft Models for Speculative Decoding

Researchers introduce Osprey, a pre-training approach for draft models used in speculative decoding that is not tied to any specific target model. Draft models are typically tuned to a single target's output distribution, causing acceptance rates to fall when workloads change, and target-agnostic pre-training aims to keep them robust. The work focuses on achieving faster and more stable inference for large language models.

speculative-decoding/llm-inference/efficiency/research