Acceptance Dynamics Across Cognitive Domains in Speculative Decoding

• URL: http://arxiv.org/abs/2604.14682v1
• Date: Thu, 16 Apr 2026 06:38:44 GMT
• Title: Acceptance Dynamics Across Cognitive Domains in Speculative Decoding
• Abstract summary: We present an empirical study of tree-based speculative decoding acceptance dynamics.<n>Our study spans four well-established NLP benchmark domains.<n>We find that task type is a stronger predictor of acceptance than tree depth.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Speculative decoding accelerates large language model (LLM) inference. It uses a small draft model to propose a tree of future tokens. A larger target model then verifies these tokens in a single batched forward pass. Despite the growing body of work on speculative methods, the degree to which the cognitive characteristics of a task affect acceptance probability remains largely unexplored. We present an empirical study of tree-based speculative decoding acceptance dynamics. Our study spans four well-established NLP benchmark domains: code generation, mathematical reasoning, logical reasoning, and open-ended chat. For this, we use TinyLlama-1.1B as the draft model against Llama-2-7B-Chat-GPTQ as the target. Over 99,768 speculative nodes collected from 200 prompts, we derive per-domain acceptance rates, expected accepted lengths, depth-acceptance profiles, and entropy-acceptance correlations. We find that task type is a stronger predictor of acceptance than tree depth. Furthermore, only the chat domain consistently yields an expected accepted length exceeding 1.0 token per step. We also show that the entropy-acceptance correlation is consistently negative but weak across all domains (rho in [-0.20, -0.15]). Counterintuitively, chat produces the highest entropy yet the highest acceptance rate. We attribute this divergence to the lexical predictability of RLHF-aligned register. These findings have direct implications for domain-aware speculation budgets and draft-model selection strategies. Index Terms--speculative decoding, large language model inference, tree attention, draft model, acceptance probability, LLM efficiency

Related papers

• PRESTO: Prefix-Aligned Tree Drafting for Diffusion Speculative Decoding [98.71600061506206]
  We propose PRESTO, a principled framework that extends tree-based drafting to diffusion drafters.<n> PRESTO achieves up to an average of $1.5times$ end-to-end throughput speedup on the state-of-the-art dedicated diffusion drafter SD.
  arXiv  Detail & Related papers  (2026-06-20T14:32:14Z)
• TAPS: Target-Aware Prefix Tree Selection for Diffusion-Drafted Speculative Decoding [4.5881678566958985]
  Using a diffusion model for parallel drafting is a promising approach for speculative decoding.<n>This shifts the bottleneck to verification: verifying a single sequence limits acceptance length, while verifying large draft trees incurs excessive target-model latency.<n>We propose TAPS, a target-aware prefix selection method that turns diffusion marginals into path-conditioned acceptance estimates.
  arXiv  Detail & Related papers  (2026-05-30T02:39:40Z)
• Learn from your own latents and not from tokens: A sample-complexity theory [53.5821824211418]
  We study networks trained to predict their emphown latent representations of related views or masked regions.<n>We show that latent prediction achieves this with a number of samples emphconstant in $L$, up to logarithmic factors.<n>This suggests that explicit stacking such as H-JEPA is largely redundant.
  arXiv  Detail & Related papers  (2026-05-26T22:16:42Z)
• Pretraining Language Models to Ponder in Continuous Space [50.52734567589996]
  We introduce this pondering process into language models by repeatedly invoking the forward process within a single token generation step.<n>We show that the model can learn to ponder in this way through self-supervised learning, without any human annotations.
  arXiv  Detail & Related papers  (2025-05-27T03:47:33Z)
• Traversal Verification for Speculative Tree Decoding [15.720388162422978]
  Speculative decoding is a promising approach for accelerating large language models.<n>This paper introduces Traversal Verification, a novel speculative decoding algorithm.<n>We show that our method consistently improves acceptance length and throughput over existing methods.
  arXiv  Detail & Related papers  (2025-05-18T12:51:55Z)
• FIRP: Faster LLM inference via future intermediate representation prediction [54.897493351694195]
  FIRP generates multiple tokens instead of one at each decoding step. We conduct extensive experiments, showing a speedup ratio of 1.9x-3x in several models and datasets.
  arXiv  Detail & Related papers  (2024-10-27T15:53:49Z)
• Graph-Structured Speculative Decoding [52.94367724136063]
  Speculative decoding has emerged as a promising technique to accelerate the inference of Large Language Models. We introduce an innovative approach utilizing a directed acyclic graph (DAG) to manage the drafted hypotheses. We observe a remarkable speedup of 1.73$times$ to 1.96$times$, significantly surpassing standard speculative decoding.
  arXiv  Detail & Related papers  (2024-07-23T06:21:24Z)
• SpecDec++: Boosting Speculative Decoding via Adaptive Candidate Lengths [41.863092939498316]
  Speculative decoding reduces the inference latency of a target large language model via utilizing a smaller and faster draft model.<n>We study the choice of the candidate length K and formulate it as a Markov Decision Process.<n>Motivated by this theory, we propose SpecDec++, an enhanced version of speculative decoding that adaptively determines the candidate length on the fly.
  arXiv  Detail & Related papers  (2024-05-30T05:49:38Z)
• Online Speculative Decoding [34.987825705622555]
  We introduce online speculative decoding to accelerate the inference of large language models. The main idea is to continuously update the (multiple) draft model(s) on observed user query data. We develop a prototype of online speculative decoding based on knowledge distillation and evaluate it using both synthetic and real query data.
  arXiv  Detail & Related papers  (2023-10-11T04:03:42Z)
• Interpretability at Scale: Identifying Causal Mechanisms in Alpaca [62.65877150123775]
  We use Boundless DAS to efficiently search for interpretable causal structure in large language models while they follow instructions. Our findings mark a first step toward faithfully understanding the inner-workings of our ever-growing and most widely deployed language models.
  arXiv  Detail & Related papers  (2023-05-15T17:15:40Z)
• Complex Event Forecasting with Prediction Suffix Trees: Extended Technical Report [70.7321040534471]
  Complex Event Recognition (CER) systems have become popular in the past two decades due to their ability to "instantly" detect patterns on real-time streams of events. There is a lack of methods for forecasting when a pattern might occur before such an occurrence is actually detected by a CER engine. We present a formal framework that attempts to address the issue of Complex Event Forecasting.
  arXiv  Detail & Related papers  (2021-09-01T09:52:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.