arxiv: 2604.11838 · v1 · submitted 2026-04-12 · 💻 cs.LG · cs.AI

Recognition: unknown

A Layer-wise Analysis of Supervised Fine-Tuning

Qinghua Zhao , Xueling Gong , Xinyu Chen , Zhongfeng Kang , Xinlu Li

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Pith reviewed 2026-05-10 15:59 UTC · model grok-4.3

classification 💻 cs.LG cs.AI

keywords supervised fine-tuninglayer-wise analysismodel alignmentefficient tuningcatastrophic forgettinglanguage model layers

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The pith

Final layers change most during supervised fine-tuning while middle layers remain stable.

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

The paper tracks how supervised fine-tuning alters language models layer by layer using information, geometry, and optimization measures across several model sizes. It finds that layers from 20 to 80 percent depth hold steady while the final layers shift rapidly. From this pattern the authors build a selective tuning method that updates only the stable middle blocks. The approach matches or exceeds full low-rank adaptation on math tasks while changing far fewer parameters. A reader would care because the result points to a way to add instruction following without erasing earlier knowledge across the whole network.

Core claim

Layer-wise measurements show middle layers (20 percent to 80 percent) stay stable under supervised fine-tuning while final layers display high sensitivity; selectively tuning only the intermediate blocks therefore produces effective alignment that is localized rather than spread across the model.

What carries the argument

Mid-Block Efficient Tuning, which updates only the intermediate layers identified as stable by the depth-dependent pattern.

If this is right

Alignment can be achieved by changing only a subset of layers instead of the full model.
Selective middle-block updates reduce the number of parameters that must be modified compared with standard low-rank methods.
Instruction-following ability emerges mainly through changes in the final layers.
The localized nature of the changes suggests catastrophic forgetting can be limited by avoiding updates to stable middle layers.

Where Pith is reading between the lines

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

If the pattern scales, training pipelines could freeze middle layers by default and focus compute only on the ends.
The same layer-wise measurements might reveal similar localization in other training stages such as preference tuning.
Designers could test whether inserting fixed middle blocks into new architectures reduces the cost of later alignment steps.

Load-bearing premise

The observed stability in middle layers and sensitivity in final layers during fine-tuning will appear in other model sizes, tasks, and alignment settings.

What would settle it

A new experiment on a different model scale or task that finds uniform sensitivity across all layers rather than the reported middle-layer stability would falsify the central pattern.

Figures

Figures reproduced from arXiv: 2604.11838 by Qinghua Zhao, Xinlu Li, Xinyu Chen, Xueling Gong, Zhongfeng Kang.

**Figure 2.** Figure 2: Intrinsic representation metrics across layers for Base and SFT models. [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗

**Figure 3.** Figure 3: Next-token prediction accuracy of layer-wise probing, from left to right is GSM8K, MMLU, IFEval, [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

**Figure 5.** Figure 5: Correlation between layer-wise magnitude of [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗

**Figure 7.** Figure 7: Mid-Block Efficient Tuning, from left to right [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗

read the original abstract

While critical for alignment, Supervised Fine-Tuning (SFT) incurs the risk of catastrophic forgetting, yet the layer-wise emergence of instruction-following capabilities remains elusive. We investigate this mechanism via a comprehensive analysis utilizing information-theoretic, geometric, and optimization metrics across model scales (1B-32B). Our experiments reveal a distinct depth-dependent pattern: middle layers (20\%-80\%) are stable, whereas final layers exhibit high sensitivity. Leveraging this insight, we propose Mid-Block Efficient Tuning, which selectively updates these critical intermediate layers. Empirically, our method outperforms standard LoRA up to 10.2\% on GSM8K (OLMo2-7B) with reduced parameter overhead, demonstrating that effective alignment is architecturally localized rather than distributed. The code is publicly available at https://anonymous.4open.science/r/base_sft.

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

The paper observes middle-layer stability during SFT across metrics and scales, but its claim of architectural localization rests on a comparison that does not control for full-rank updates versus low-rank adapters or matched parameter budgets.

read the letter

The paper measures how supervised fine-tuning changes layers using information-theoretic, geometric, and optimization metrics on models from 1B to 32B. It reports that layers in the 20-80% range remain relatively stable while the final layers shift more, then introduces Mid-Block Efficient Tuning that targets full updates on that middle block. The method beats standard LoRA by as much as 10.2% on GSM8K for OLMo2-7B while using fewer parameters, and the authors release the code.

Referee Report

2 major / 2 minor

Summary. The paper performs a layer-wise analysis of Supervised Fine-Tuning (SFT) across model scales (1B-32B) using information-theoretic, geometric, and optimization metrics. It reports a depth-dependent pattern in which middle layers (20%-80%) remain stable while final layers show high sensitivity, and introduces Mid-Block Efficient Tuning that performs full updates only on the middle block. Experiments claim this method outperforms standard LoRA by up to 10.2% on GSM8K (OLMo2-7B) with lower parameter overhead, supporting the conclusion that effective alignment is architecturally localized rather than distributed.

Significance. If the reported layer-wise stability pattern and the performance advantage of selective middle-block updates hold under proper controls, the work could inform more parameter-efficient alignment procedures that reduce catastrophic forgetting and computational cost by concentrating updates where they matter most. Public code release aids reproducibility.

major comments (2)

[Experiments / Results] The central empirical claim that Mid-Block Efficient Tuning outperforms LoRA because alignment is localized to middle layers (rather than distributed) is not supported by the necessary controls. Standard LoRA applies low-rank adapters to all layers while Mid-Block uses full-rank updates on a 20-80% subset; no ablation applies an equivalent parameter budget via full updates to early layers, final layers, or randomly chosen blocks. This leaves open the possibility that gains arise from update rank or total parameter count rather than layer selection (see results comparing the two methods).
[Abstract and Section 4 (Metrics)] The abstract and main claims assert a clear depth-dependent stability/sensitivity pattern across multiple metrics and scales, yet the manuscript supplies no details on exact metric definitions, statistical tests, data splits, or controls for model size/task variation. Without these, it is impossible to verify whether the measurements actually support the localization conclusion.

minor comments (2)

[Introduction] Notation for layer ranges (e.g., '20%-80%') should be defined explicitly with respect to total depth in the first section where it appears.
[Abstract] The paper states code is available at an anonymous link; a permanent repository or DOI should be provided for archival purposes.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our layer-wise analysis of SFT and the proposed Mid-Block Efficient Tuning method. The comments highlight important areas for strengthening empirical controls and metric transparency. We address each major comment below and will incorporate revisions to improve the manuscript.

read point-by-point responses

Referee: [Experiments / Results] The central empirical claim that Mid-Block Efficient Tuning outperforms LoRA because alignment is localized to middle layers (rather than distributed) is not supported by the necessary controls. Standard LoRA applies low-rank adapters to all layers while Mid-Block uses full-rank updates on a 20-80% subset; no ablation applies an equivalent parameter budget via full updates to early layers, final layers, or randomly chosen blocks. This leaves open the possibility that gains arise from update rank or total parameter count rather than layer selection (see results comparing the two methods).

Authors: We acknowledge that the current comparison between Mid-Block Efficient Tuning (full-rank updates on the 20-80% block) and standard LoRA (low-rank adapters across all layers) does not isolate layer selection from differences in update rank or total parameter count. This is a valid concern, as the performance advantage (up to 10.2% on GSM8K) could partly stem from using full updates rather than the specific localization. Our primary evidence for localization comes from the multi-metric stability analysis showing middle layers remain stable while final layers are sensitive. To directly address the gap, we will add ablations in the revised version applying full-parameter updates to early layers (0-20%), late layers (80-100%), and randomly selected blocks, all with parameter budgets matched to Mid-Block. These controls will clarify whether the middle-block choice drives the gains beyond rank or count effects. revision: yes
Referee: [Abstract and Section 4 (Metrics)] The abstract and main claims assert a clear depth-dependent stability/sensitivity pattern across multiple metrics and scales, yet the manuscript supplies no details on exact metric definitions, statistical tests, data splits, or controls for model size/task variation. Without these, it is impossible to verify whether the measurements actually support the localization conclusion.

Authors: We agree that the abstract and Section 4 would benefit from greater explicitness to allow verification of the depth-dependent pattern. While the manuscript describes the use of information-theoretic, geometric, and optimization metrics across 1B-32B scales, we will expand the revision to include: precise mathematical definitions and formulas for each metric; details on statistical tests (e.g., significance thresholds and multiple-run protocols); explicit data splits and preprocessing from the benchmarks; and additional discussion of controls for model size and task variation. This will make the reported stability in middle layers (20-80%) and sensitivity in final layers fully reproducible and verifiable. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical claims rest on direct measurements

full rationale

The paper conducts layer-wise analysis of SFT using information-theoretic, geometric, and optimization metrics across 1B-32B models, identifies middle-layer stability (20%-80%) versus final-layer sensitivity via these measurements, proposes Mid-Block Efficient Tuning on that basis, and validates via direct empirical comparison to LoRA (e.g., +10.2% on GSM8K). No equations, derivations, or predictions reduce to fitted inputs by construction. No self-citations, uniqueness theorems, or ansatzes are invoked as load-bearing premises. The localization conclusion follows from the reported performance differentials rather than definitional equivalence. This is a standard self-contained empirical study.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The central claim is grounded in empirical observations from experiments on models of varying sizes; the abstract introduces no explicit free parameters, background axioms, or new postulated entities.

pith-pipeline@v0.9.0 · 5452 in / 1168 out tokens · 41079 ms · 2026-05-10T15:59:33.815895+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.

Crafting Reversible SFT Behaviors in Large Language Models
cs.LG 2026-05 unverdicted novelty 8.0

LCDD creates sparse carriers for SFT behaviors that SFT-Eraser can reverse, with ablations showing the sparse structure enables causal control.
Rotation-Preserving Supervised Fine-Tuning
cs.LG 2026-05 unverdicted novelty 6.0

RPSFT improves the in-domain versus out-of-domain performance trade-off during LLM supervised fine-tuning by penalizing rotations in pretrained singular subspaces as a proxy for loss-sensitive directions.

Reference graph

Works this paper leans on

6 extracted references · 2 canonical work pages · cited by 2 Pith papers

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