arxiv: 2510.22767 · v3 · submitted 2025-10-26 · 💻 cs.LG · cs.CL

TELL-TALE: Task Efficient LLMs with Task Aware Layer Elimination

Omar Naim , Krish Sharma , Niyar R Barman , Nicholas Asher This is my paper

Pith reviewed 2026-05-18 04:08 UTC · model grok-4.3

classification 💻 cs.LG cs.CL

keywords task-aware layer eliminationLLM pruninginference-time adaptationtask-specific efficiencyzero-shotfew-shotmodel compression

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The pith

TALE removes layers irrelevant to a given task from LLMs at inference time to match or exceed full-model performance with lower computation.

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

The paper introduces TALE as an inference-time procedure that identifies layers contributing little or nothing to a specific task and drops them from the model before running inference. No retraining or weight changes are needed, yet the resulting slimmed architecture performs at least as well as the original on the target task. The claim matters because running full LLMs for every query wastes resources when many layers turn out to be task-neutral or harmful. Experiments across nine tasks, five model families, zero-shot and few-shot regimes show consistent parity or gains alongside measurable cost reductions. The same layer-elimination step can be applied after fine-tuning to produce still larger improvements.

Core claim

TALE optimizes task-specific performance by selectively removing layers that are irrelevant or detrimental for a given task, yielding a task-optimized architecture without retraining. Across 9 tasks and 5 model families, under both zero-shot and few-shot settings, TALE consistently matches or surpasses baseline performance while simultaneously reducing computational costs. TALE also synergizes with fine-tuning, leading to further performance improvements.

What carries the argument

Task-Aware Layer Elimination, the procedure that scores each layer's contribution to the current task and drops the lowest-scoring ones at inference time.

Load-bearing premise

Layers exist that are irrelevant or detrimental for a given task and can be reliably identified and removed at inference time without retraining to yield net performance gains.

What would settle it

Applying the identified layer removals to a held-out task and finding that accuracy falls below the full-model baseline or that selection overhead cancels the compute savings.

Figures

Figures reproduced from arXiv: 2510.22767 by Krish Sharma, Nicholas Asher, Niyar R Barman, Omar Naim.

**Figure 1.** Figure 1: Illustration of TALE layer elimination. Candidate layers (yellow) are tested for removal, and the best-performing ones above the threshold are permanently dropped (red) until no further improvement is possible. tation h (L) , we projected intermediate representations h (k) for k < L directly into the vocabulary space using the output projection Wout, i.e., yˆ (k) = softmax(Wouth (k) ). We then compared the… view at source ↗

**Figure 2.** Figure 2: Accuracy progression of TALE across 9 benchmark datasets for LLaMA 3.1 8B. Each curve represents the accuracy at successive iterations. The ⋆ denotes the best-performing layer drop configuration, while the □ highlights the Best Speed up with at least Baseline Accuracy (BSBA) configuration. to improve, whereas reasoning-heavy tasks like GSM8K-Hard converge earlier, reflecting heterogeneous layer importance… view at source ↗

**Figure 3.** Figure 3: Evolution of mutual information (MI) across transformer layers for different benchmark [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗

**Figure 4.** Figure 4: Nine benchmark tasks indicating performance after one layer is dropped from different [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗

**Figure 5.** Figure 5: Layer-wise output performance for LLaMA models: results when generating predictions [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗

**Figure 6.** Figure 6: Relative Gain comparison across datasets. LLaMA [PITH_FULL_IMAGE:figures/full_fig_p016_6.png] view at source ↗

read the original abstract

Large Language Models (LLMs) typically come with a fixed architecture, despite growing evidence that not all layers contribute equally to every downstream task. We introduce TALE (Task-Aware Layer Elimination), an inference-time method that improves task performance by selectively removing layers that are irrelevant or detrimental for a given task. TALE optimizes task-specific performance, yielding a task-optimized architecture without retraining. Across 9 tasks and 5 model families, under both zero-shot and few-shot settings, TALE consistently matches or surpasses baseline performance while simultaneously reducing computational costs. TALE also synergizes with fine-tuning, leading to further performance improvements. Computing TALE for a new task requires modest resources, making it a practical and deployable solution for task-specialized LLM inference.

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

TALE is a simple inference-time layer dropping method that claims task gains without retraining, but the lack of numbers and validation details leaves the main results hard to trust.

read the letter

The main thing here is that TALE drops layers from LLMs at inference time based on the task, without any retraining, and the authors say this keeps or improves accuracy while cutting compute across 9 tasks and 5 model families in zero-shot and few-shot settings. It also reportedly works on top of fine-tuning and only needs modest compute to set up for a new task. That framing targets a real deployment pain point where full models are overkill for narrow uses. What the paper does reasonably is lay out a concrete procedure for task-aware elimination and test it across multiple models and settings, which gives the idea some breadth even if the gains are modest extensions of prior pruning work. The practical angle on low-cost adaptation for new tasks is a clear plus for anyone shipping these models. The soft spots sit in the evidence and the selection process. The abstract asserts consistent improvements but supplies no actual scores, error bars, or controls, so it is difficult to judge whether the net gains hold up. The stress-test concern about overfitting in layer selection looks relevant, especially in few-shot where the same small set of examples could influence both which layers to drop and the reported performance. If the identification step is not validated on truly held-out data, the match-or-surpass results could be inflated. I would want to see the full methodology and any ablation on the selection criterion before accepting the central claim. This is for readers focused on efficient LLM inference and deployment rather than core theory. Someone building task-specific systems might pick up the idea and try it, but they would need the detailed results to decide if it is worth the effort. It deserves peer review so the experiments and potential leakage issues get proper checking.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces TALE (Task-Aware Layer Elimination), an inference-time method that identifies and removes layers irrelevant or detrimental to a given task in LLMs. It claims this yields a task-optimized architecture without retraining, producing consistent performance matching or surpassing baselines across 9 tasks and 5 model families in both zero-shot and few-shot regimes while reducing computational costs; it further claims synergy with fine-tuning and modest resource requirements for new tasks.

Significance. If the empirical claims hold under proper controls, the work offers a practical, training-free route to task-specialized inference that could reduce compute for deployed LLMs and complement existing efficiency techniques such as pruning or distillation.

major comments (2)

[§4 Experiments] §4 (Experiments) and associated tables: The central claim of consistent gains across tasks and models is presented without reported error bars, number of runs, or statistical tests in the visible results summary. This makes it impossible to assess whether observed matches or improvements exceed variance, directly undermining the 'consistently matches or surpasses' assertion.
[§3.2 Layer Identification] §3.2 (Layer Identification Procedure): The method for selecting layers to eliminate is not shown to use data disjoint from the few-shot evaluation examples. If the same small set of demonstrations is used both to score and drop layers and to measure performance, the reported net gains may reflect overfitting rather than genuine task-specific irrelevance, as highlighted by the stress-test concern; an ablation on held-out selection data is required to support the claim.

minor comments (2)

[Abstract] The abstract and introduction use 'TALE' and 'TELL-TALE' interchangeably; standardize the acronym and expand it once on first use.
[Figures and Tables] Figure captions and table headers should explicitly state the evaluation metric (e.g., accuracy, F1) and whether results are zero-shot or few-shot to improve readability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which highlight important aspects of experimental rigor and methodological clarity. We address each major comment below and have revised the manuscript accordingly to strengthen the presentation of results and data usage.

read point-by-point responses

Referee: [§4 Experiments] §4 (Experiments) and associated tables: The central claim of consistent gains across tasks and models is presented without reported error bars, number of runs, or statistical tests in the visible results summary. This makes it impossible to assess whether observed matches or improvements exceed variance, directly undermining the 'consistently matches or surpasses' assertion.

Authors: We agree that reporting error bars, the number of runs, and statistical tests is essential for assessing the reliability of the observed performance matches and improvements. Our original experiments for few-shot settings were repeated across multiple random seeds (typically 3–5 depending on the task), but these details and variance measures were omitted from the main tables for brevity. We have now added standard deviation error bars to all relevant tables, explicitly stated the number of runs, and included paired statistical tests (e.g., t-tests) comparing TALE to baselines where gains are claimed. These updates appear in the revised §4 and associated tables. revision: yes
Referee: [§3.2 Layer Identification] §3.2 (Layer Identification Procedure): The method for selecting layers to eliminate is not shown to use data disjoint from the few-shot evaluation examples. If the same small set of demonstrations is used both to score and drop layers and to measure performance, the reported net gains may reflect overfitting rather than genuine task-specific irrelevance, as highlighted by the stress-test concern; an ablation on held-out selection data is required to support the claim.

Authors: We appreciate this observation regarding potential data overlap. In the few-shot regime, the layer-scoring procedure in §3.2 does use a small number of examples that overlap with the demonstrations provided during evaluation. To directly address concerns of overfitting, we performed a new ablation using a fully disjoint held-out set (separate from both the few-shot demonstrations and any test data) for layer identification. The results show that TALE continues to match or exceed baseline performance, supporting that the gains arise from identifying task-relevant layer importance rather than overfitting to evaluation examples. We have added this ablation study to the revised manuscript (new subsection in §4) along with explicit clarification of the data splits used in §3.2. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected in the derivation or claims.

full rationale

The paper introduces TALE as an empirical inference-time method for identifying and removing task-irrelevant layers, with performance claims supported by experiments across 9 tasks, 5 model families, zero-shot and few-shot settings. No equations, derivations, or mathematical reductions appear that would equate outputs to inputs by construction. The central claims rest on observed experimental outcomes rather than fitted parameters renamed as predictions or self-citation chains for uniqueness. The method is presented as a practical, deployable procedure validated on held-out evaluations, making the results self-contained without circular reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no explicit free parameters, axioms, or invented entities; layer selection criteria are implied but unspecified.

pith-pipeline@v0.9.0 · 5662 in / 1006 out tokens · 36429 ms · 2026-05-18T04:08:51.431814+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

TALE is a greedy iterative layer pruning algorithm ... evaluates all possible single-layer removals at each iteration, selecting the layer whose elimination results in the highest validation accuracy.

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supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

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