arxiv: 2604.14612 · v1 · submitted 2026-04-16 · 💻 cs.LG · cs.CL

Recognition: unknown

ConfLayers: Adaptive Confidence-based Layer Skipping for Self-Speculative Decoding

Walaa Amer , Uday das , Fadi Kurdahi

Authors on Pith no claims yet

Pith reviewed 2026-05-10 11:36 UTC · model grok-4.3

classification 💻 cs.LG cs.CL

keywords self-speculative decodinglayer skippingconfidence scoresadaptive thresholdLLM inferencedraft modelspeculative decoding

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The pith

Confidence scores guide dynamic layer skipping to form draft models that speed up LLM generation by up to 1.4 times.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents ConfLayers as a plug-and-play method for self-speculative decoding that builds a smaller draft model on the fly by skipping selected intermediate layers. It does this through repeated computation of confidence scores, choice of skips via an adaptive threshold, performance checks on the resulting draft, and updates to the skip set until gains stop or an iteration limit is hit. This setup is meant to avoid the training cost of learned skipping policies while still adapting to different tasks and inputs. A reader would care if it holds because standard LLM generation remains slow, and a lightweight way to trade a little computation for reliable speed without quality loss could expand where these models run effectively.

Core claim

ConfLayers creates the draft model for self-speculative decoding by iteratively calculating confidence scores across layers, applying an adaptive threshold to choose which layers to skip, evaluating the quality and speed of the resulting subnetwork, and refining the selection until no further improvement occurs or a maximum iteration count is reached. This produces an adaptive draft without any separate training step for the skipping decisions.

What carries the argument

The iterative confidence-score computation and adaptive-threshold selection loop that optimizes which layers to skip when forming the draft model.

If this is right

The method reaches up to 1.4x speedup over ordinary LLM generation on the tested models and datasets.
It delivers steadier speed versus quality results than fixed heuristics or separately trained skipping policies.
No training run is required to learn which layers to skip, removing that source of overhead.
The draft model remains able to adjust to new tasks and data distributions through the runtime selection process.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The same confidence-driven loop could be applied to other model components such as attention heads or embedding layers for additional gains.
Hardware-specific tuning of the iteration limit or threshold might further reduce latency on edge devices.
The approach may combine with existing speculative decoding variants that use different draft sources to compound the speed benefit.

Load-bearing premise

That confidence scores computed at intermediate layers will point to skip choices that keep the draft model close enough to the full model for efficient correction during verification.

What would settle it

A direct comparison on a held-out model or dataset where ConfLayers either produces no net speedup, runs slower than standard generation, or yields lower output quality than vanilla self-speculative decoding or other skipping heuristics.

Figures

Figures reproduced from arXiv: 2604.14612 by Fadi Kurdahi, Uday das, Walaa Amer.

**Figure 1.** Figure 1: ConfLayers, a self-speculative decoding framework with layer skipping, offers up to 1.4× speedup compared to vanilla LLM generation. 1. Introduction The increasing scale and capability of large language models (LLMs) have transformed natural language processing, enabling strong performance across tasks such as question answering, summarization, machine translation, and reasoning. Modern LLMs, often compr… view at source ↗

**Figure 2.** Figure 2: ConfLayers framework implements speculative decoding in a 3-step process. The generation step will use the current non-skip layer set to form the draft model and generated candidate tokens that will be verified via the target model. The layer optimization search step relies on confidence-based layer skipping to form the non-skip layer set. The search check step connects the two previous steps by ensuring t… view at source ↗

**Figure 3.** Figure 3: Confidence computation detailed. ation process of all remaining prompts. Otherwise, another round of search for the best draft model layer set is initiated. 3.1. Confidence-based Layer Skipping The normalized confidence scores of the layers are found from the model logits by leveraging the relationship between predictive entropy and uncertainty [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

**Figure 4.** Figure 4: Speedup comparison between SWIFT and ConfLayers with LLaMa-2-13B on a dynamic input stream. speedup for inference with CodeLLaMa-34B and a speedup of 1.15× for inference with Qwen2.5-Math-72B with a remarkably lower number of accepted tokens than ConfLayers in both cases. Finally, the Rouge−2 results fall in high overlap ranges relative to the tasks deployed, which expected from speculative decoding meth… view at source ↗

**Figure 6.** Figure 6: Speedup at every input prompt for different windowing techniques on (a) LLaMa-2-13B and (b) LLaMa-2-70B. is less than 0.9×. This shows that using a constant window size across all layers fails to deliver an improvement over vanilla generation. Although we observe better results when a constant window size is adopted When inference is run with LLaMa-2-70B, we notice an inconsistency across models [PITH_FU… view at source ↗

**Figure 7.** Figure 7: Speedup at every input prompt for different λ values on Alpaca with (a) LLaMa-2-13B and (b) LLaMa-3-8B. B. Correlation Visualization Example We visualize the correlation between these different variables for one round of the layer set search process with the LLaMa2-13B model over the CNN-DailyMail (CNN-DM) dataset in [PITH_FULL_IMAGE:figures/full_fig_p011_7.png] view at source ↗

**Figure 8.** Figure 8: Confidence, local mean, gradient values, and the window size for every layer with LLaMa-2-13B on GSM8K. SWIFT struggles to achieve speedup in cases like inference with LLaMa-2-13B on CNN-DM, ConfLayers is still able to accelerate inference by 1.162×. This improvement is due to a key difference between ConfLayers and SWIFT, which is the objective function. ConfLayers prioritizes maximizing the number of acc… view at source ↗

**Figure 9.** Figure 9: Progression of the best skipped layer set (in red) with LLaMa-2-13B on GSM8K. 13 [PITH_FULL_IMAGE:figures/full_fig_p013_9.png] view at source ↗

read the original abstract

Self-speculative decoding is an inference technique for large language models designed to speed up generation without sacrificing output quality. It combines fast, approximate decoding using a compact version of the model as a draft model with selective re-evaluation by the full target model. Some existing methods form the draft model by dynamically learning which layers to skip during inference, effectively creating a smaller subnetwork to speed up computation. However, using heuristic-based approaches to select layers to skip can often be simpler and more effective. In this paper, we propose ConfLayers, a dynamic plug-and-play approach to forming the draft model in self-speculative decoding via confidence-based intermediate layer skipping. The process iteratively computes confidence scores for all layers, selects layers to skip based on an adaptive threshold, evaluates the performance of the resulting set, and updates the best selection until no further improvement is achieved or a maximum number of iterations is reached. This framework avoids the overhead and complexity of training a layer skipping policy and can provide more consistent speed-quality trade-offs while preserving the adaptivity of the draft model to diverse tasks and datasets. The performance evaluation of ConfLayers across different models and datasets shows that our novel approach offers up to 1.4x speedup over vanilla LLM generation.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

ConfLayers adds an iterative confidence-based loop for picking draft layers in self-speculative decoding, but the 1.4x speedup claim sits on thin evidence and unaddressed runtime cost.

read the letter

ConfLayers describes a training-free way to build the draft model inside self-speculative decoding. It runs an iterative loop that scores every layer by confidence, applies an adaptive threshold to drop some layers, tests the resulting subnetwork, and keeps the best configuration until it stops improving or hits the iteration cap. The main point to take away is that this produces an adaptive draft without a learned policy, and the abstract asserts up to 1.4x speedup over plain generation on various models and datasets. That combination of adaptivity and no training cost is the concrete new piece relative to the heuristic or trained baselines referenced in the abstract. The framing is straightforward and avoids the usual training overhead that other layer-skipping methods carry. The paper also notes that the method stays plug-and-play and aims for steadier speed-quality trade-offs across tasks. Those are reasonable goals for inference work. The soft spots are more noticeable. The speedup number appears without any experimental section, baselines, datasets, quality metrics, or variance numbers in the abstract, so the claim cannot be checked yet. The iterative search itself runs at inference time; if the performance evaluation step inside the loop needs extra forward passes or token checks, that cost is paid on every generation and could cancel part of the reported gain. The abstract contrasts the method only with training costs and stays silent on this runtime overhead. The central argument therefore rests on an assumption that the search is cheap enough to be net positive, which is not demonstrated. This work sits in the efficient LLM inference area. A reader already working on speculative decoding or layer pruning would see a clear incremental idea and might test the loop themselves. It is not yet strong enough for a broad audience. The paper deserves peer review because the underlying problem is real and the method is simple enough that referees can quickly judge whether the experiments close the gaps on overhead and measurement. I would send it out rather than desk reject, with the expectation that the authors supply the missing controls and a direct comparison to fixed heuristics.

Referee Report

2 major / 1 minor

Summary. The paper proposes ConfLayers, a plug-and-play method for self-speculative decoding that forms an adaptive draft model by iteratively computing per-layer confidence scores, applying an adaptive threshold to select layers to skip, evaluating the resulting subnetwork's performance, and retaining the best selection until convergence or an iteration limit. It claims this training-free approach yields more consistent speed-quality trade-offs than learned policies and delivers up to 1.4x speedup over vanilla LLM generation across models and datasets.

Significance. If the net speedup claim holds after rigorous accounting for search overhead and with proper baselines, the work could offer a practical alternative to trained layer-skipping policies for LLM inference acceleration, emphasizing adaptivity without training costs.

major comments (2)

[Method] Method section (iterative selection procedure): the 'evaluate performance' step in the loop is described only at a high level; it is unclear whether this requires extra forward passes, token generation, or quality metrics on candidate drafts at inference time. Without explicit cost accounting, the 1.4x speedup cannot be confirmed as a net gain over vanilla or fixed-heuristic baselines.
[Experiments] Experiments section: the central claim of 'up to 1.4x speedup' is stated without reported models, datasets, exact baselines (including the heuristic methods the abstract itself calls 'simpler and more effective'), measurement protocol (tokens/s including search cost), or error analysis. This renders the performance evaluation unverifiable from the provided details.

minor comments (1)

[Abstract] Abstract: the phrase 'preserving the adaptivity of the draft model to diverse tasks and datasets' is repeated without concrete examples or metrics; move supporting evidence to the experiments section.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on our manuscript. We address each major comment point by point below and will revise the paper to improve clarity and verifiability of the claims.

read point-by-point responses

Referee: [Method] Method section (iterative selection procedure): the 'evaluate performance' step in the loop is described only at a high level; it is unclear whether this requires extra forward passes, token generation, or quality metrics on candidate drafts at inference time. Without explicit cost accounting, the 1.4x speedup cannot be confirmed as a net gain over vanilla or fixed-heuristic baselines.

Authors: We agree that the 'evaluate performance' step is described at a high level and requires explicit elaboration. In the revised manuscript we will expand the method section to detail exactly how performance is assessed (including any forward passes or metrics on candidate subnetworks), specify whether this occurs in an offline selection phase or online, and provide a full cost breakdown showing that the overhead is amortized such that the reported net speedup holds relative to vanilla generation and heuristic baselines. revision: yes
Referee: [Experiments] Experiments section: the central claim of 'up to 1.4x speedup' is stated without reported models, datasets, exact baselines (including the heuristic methods the abstract itself calls 'simpler and more effective'), measurement protocol (tokens/s including search cost), or error analysis. This renders the performance evaluation unverifiable from the provided details.

Authors: We acknowledge that the experiments section lacks sufficient detail for independent verification. The revised version will explicitly list the models and sizes tested, the datasets, the precise heuristic baselines referenced in the abstract, the full measurement protocol (tokens per second inclusive of all search and selection costs), and any error analysis or variance reporting. These additions will be presented in tables and text to substantiate the 1.4x speedup claim. revision: yes

Circularity Check

0 steps flagged

No circularity: heuristic procedure with empirical evaluation only

full rationale

The paper describes ConfLayers as a plug-and-play iterative procedure: compute per-layer confidence scores, apply adaptive threshold to select skips, evaluate the resulting subnetwork performance, and retain the best set until convergence or iteration limit. No equations, derivations, or first-principles results appear that reduce to their own inputs by construction. Speedup claims (up to 1.4x) rest on direct empirical measurement across models/datasets rather than any fitted parameter renamed as prediction or self-referential definition. The method is self-contained against external benchmarks and does not invoke load-bearing self-citations or uniqueness theorems.

Axiom & Free-Parameter Ledger

2 free parameters · 1 axioms · 0 invented entities

The method rests on the domain assumption that intermediate-layer confidence scores can serve as a reliable proxy for skippable computation; two procedural parameters (adaptive threshold and iteration budget) are introduced but not numerically specified in the abstract.

free parameters (2)

adaptive threshold
Controls which layers are skipped based on per-layer confidence; its value is updated during the iterative search.
maximum number of iterations
Limits the search for the best layer-skipping configuration.

axioms (1)

domain assumption Confidence scores computed at intermediate layers indicate which layers can be skipped without substantial degradation of final output quality.
This assumption underpins the entire skipping decision process described in the abstract.

pith-pipeline@v0.9.0 · 5518 in / 1310 out tokens · 52983 ms · 2026-05-10T11:36:34.726626+00:00 · methodology

discussion (0)

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

Component-Aware Self-Speculative Decoding in Hybrid Language Models
cs.CL 2026-05 unverdicted novelty 7.0

Component-aware self-speculative decoding achieves high acceptance rates in parallel hybrid models like Falcon-H1 but fails in sequential ones like Qwen3.5, with the gap tied to how components are integrated.
BEAM: Binary Expert Activation Masking for Dynamic Routing in MoE
cs.AI 2026-05 conditional novelty 6.0

BEAM uses binary expert activation masks trained end-to-end to achieve dynamic sparsity in MoE models, cutting FLOPs by 85% with over 98% performance retention.

Reference graph

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