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arxiv: 2605.23171 · v1 · pith:2HY3YOD7new · submitted 2026-05-22 · 💻 cs.LG · cs.AI· stat.ML

Understanding and Improving Noisy Embedding Techniques in Instruction Finetuning

Abhay Yadav This is my paper

Pith reviewed 2026-05-25 04:49 UTC · model grok-4.3

classification 💻 cs.LG cs.AIstat.ML
keywords noisy embeddingsinstruction finetuningsymmetric noiselocal curvaturelanguage modelsNEFTuneSymNoiseAlpacaEval
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The pith

Symmetric noise in embeddings improves instruction finetuning by more stringently regulating local curvature than uniform noise.

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

The paper examines why adding noise to embeddings during instruction fine-tuning helps language models, focusing on the uniform noise used in NEFTune. It finds that uniform and Gaussian noise give comparable results, then proposes symmetric noise as a new approach called SymNoise. This method is shown to produce better instruction-following models, lifting AlpacaEval scores from 64.69 percent with NEFTune to 69.04 percent on LLaMA-2-7B fine-tuned with Alpaca. The improvement is attributed to tighter control over the local curvature of the learned function. The gains hold across other models and datasets such as Evol-Instruct and ShareGPT.

Core claim

When fine-tuning the LLaMA-2-7B model using Alpaca, standard techniques yield a 29.79 percent score on AlpacaEval. However, our approach, SymNoise, increases this score significantly to 69.04 percent, using symmetric noisy embeddings. This is a 6.7 percent improvement over the state-of-the-art method, NEFTune (64.69 percent). The paper argues that symmetric noise regulates the model's local curvature more stringently than uniform noise, and that this curvature control drives the performance gains. Theoretical and empirical analysis indicates comparable performance among uniform and Gaussian noise types.

What carries the argument

Symmetric noise added to embeddings, which imposes stricter regulation on the model's local curvature during fine-tuning.

If this is right

  • SymNoise raises AlpacaEval from 64.69 percent to 69.04 percent on LLaMA-2-7B with the Alpaca dataset.
  • The method outperforms NEFTune on multiple models and on stronger instruction datasets including Evol-Instruct, ShareGPT, and OpenPlatypus.
  • Uniform and Gaussian noise produce comparable results, reducing the prior emphasis on uniform noise as uniquely effective.
  • Symmetric noise provides a concrete way to strengthen curvature control in embedding perturbations.

Where Pith is reading between the lines

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

  • Curvature control through noise symmetry could be tested as a design principle for other regularization methods beyond embeddings.
  • If the curvature mechanism holds, SymNoise might combine with other fine-tuning tricks such as data augmentation or longer training schedules.
  • The approach invites experiments on whether symmetric perturbations improve performance in domains outside language modeling, such as vision or multimodal models.

Load-bearing premise

The performance gains result from symmetric noise regulating local curvature more stringently than uniform noise does.

What would settle it

A direct measurement of local curvature on models trained with SymNoise versus NEFTune that finds no difference in curvature despite the performance gap.

Figures

Figures reproduced from arXiv: 2605.23171 by Abhay Yadav.

Figure 1
Figure 1. Figure 1: Comparison of average L2 norm ratios for Gaussian and Bernoulli noise relative to Uniform noise as a function of dimensionality. Drawing from Lemma 1 and Lemma 2, it is apparent that the expected noise from the Gaussian distribution is √ 3 times that of the Uniform distribution. Consequently, to equate the noise scales for comparison, the noise scaling factor for the Gaussian distribution should be adjuste… view at source ↗
Figure 2
Figure 2. Figure 2: Gaussian/Uniform Average L2 Norm Ratio as a Function of Dimensionality. The plot illustrates the ratio of the average L2 norm of points drawn from a Gaussian distribution to that of a Uniform distribution, with the number of points fixed at 256 and the dimensionality varying from 1 to 4096. 0 500 1000 1500 2000 2500 3000 3500 4000 Dimension d 1.75 1.80 1.85 1.90 1.95 A v era g e L2 N orm R atio Bernoulli/U… view at source ↗
Figure 3
Figure 3. Figure 3: Bernoulli/Uniform Average L2 Norm Ratio as a Function of Dimensionality. The plot depicts the ratio of the average L2 norm of points drawn from a Bernoulli distribution to that of a Uniform distribution, with the number of points fixed at 256 and the dimensionality varying from 1 to 4096. A.2.2 Average L2 Norm Ratio with Varying Number of Points 13 [PITH_FULL_IMAGE:figures/full_fig_p013_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Gaussian/Uniform Average L2 Norm Ratio for Varying Number of Points. The plot illustrates the ratio of the average L2 norm of points drawn from a Gaussian distribution to that of a Uniform distribution, with the dimensionality fixed at 4096 and the number of points varying from 64 to 256. 75 100 125 150 175 200 225 250 Number of Points 1.730 1.731 1.732 1.733 1.734 1.735 A v era g e L2 N orm R atio Bernoul… view at source ↗
Figure 5
Figure 5. Figure 5: Bernoulli/Uniform Average L2 Norm Ratio for Varying Number of Points. The plot depicts the ratio of the average L2 norm of points drawn from a Bernoulli distribution to that of a Uniform distribution, with the dimensionality fixed at 4096 and the number of points varying from 64 to 256. 14 [PITH_FULL_IMAGE:figures/full_fig_p014_5.png] view at source ↗
read the original abstract

Recent advancements in instructional fine-tuning have injected noise into embeddings, with NEFTune (Jain et al., 2024) setting benchmarks using uniform noise. Despite NEFTune's empirical findings that uniform noise outperforms Gaussian noise, the reasons for this remain unclear. This paper aims to clarify this by offering a thorough analysis, both theoretical and empirical, indicating comparable performance among these noise types. Additionally, we introduce a new fine-tuning method for language models, utilizing symmetric noise in embeddings. This method aims to enhance the model's function by more stringently regulating its local curvature, demonstrating superior performance over the current method, NEFTune. When fine-tuning the LLaMA-2-7B model using Alpaca, standard techniques yield a 29.79% score on AlpacaEval. However, our approach, SymNoise, increases this score significantly to 69.04%, using symmetric noisy embeddings. This is a 6.7% improvement over the state-of-the-art method, NEFTune (64.69%). Furthermore, when tested on various models and stronger baseline instruction datasets, such as Evol-Instruct, ShareGPT, OpenPlatypus, SymNoise consistently outperforms NEFTune. The current literature, including NEFTune, has underscored the importance of more in-depth research into the application of noise-based strategies in the fine-tuning of language models. Our approach, SymNoise, is another significant step towards this direction, showing notable improvement over the existing state-of-the-art method.

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.

Referee Report

3 major / 0 minor

Summary. The paper analyzes noise injection into embeddings during instruction fine-tuning of LLMs. It reports that uniform and Gaussian noise yield comparable performance (contrary to prior claims favoring uniform), provides theoretical analysis supporting this equivalence, and introduces SymNoise using symmetric noise, which is claimed to more stringently regulate local curvature and thereby improve model function. On LLaMA-2-7B fine-tuned with Alpaca, SymNoise reaches 69.04% on AlpacaEval (vs. 29.79% for standard fine-tuning and 64.69% for NEFTune), with consistent gains reported across other models and datasets such as Evol-Instruct and ShareGPT.

Significance. If the curvature-regulation mechanism is rigorously derived and the performance gains are shown to be reproducible with controls that isolate symmetry from other implementation factors, the work would advance understanding of noise distributions as regularizers in LLM fine-tuning and supply a practical improvement over NEFTune.

major comments (3)
  1. [Abstract] Abstract: the central claim that symmetric noise 'more stringently regulating its local curvature' produces the observed gains is unsupported; the text states that the theoretical analysis shows only that uniform and Gaussian noise are comparable, with no derivation, equation, or bound provided that demonstrates why symmetry tightens any curvature control relative to uniform noise.
  2. [Abstract] Abstract: the reported AlpacaEval scores (69.04% for SymNoise, 64.69% for NEFTune) are presented without any accompanying measurements of local curvature, Lipschitz constants, or Hessian-based quantities that would verify the proposed mechanism.
  3. [Abstract] Abstract: no ablation is described that isolates the symmetry of the noise distribution from other potential differences in implementation, hyper-parameters, or random seeds when comparing SymNoise to the NEFTune baseline.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address each of the three major comments point by point below, proposing revisions to improve clarity and rigor where the concerns are valid.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that symmetric noise 'more stringently regulating its local curvature' produces the observed gains is unsupported; the text states that the theoretical analysis shows only that uniform and Gaussian noise are comparable, with no derivation, equation, or bound provided that demonstrates why symmetry tightens any curvature control relative to uniform noise.

    Authors: The manuscript's Section 3 provides a theoretical derivation showing equivalence of uniform and Gaussian noise, followed by an extension demonstrating that symmetry enables stricter curvature regularization via zero-mean cancellation effects on the perturbation. The abstract condenses this result. We will revise the abstract to include a concise reference to the key bound derived in the theory section. revision: yes

  2. Referee: [Abstract] Abstract: the reported AlpacaEval scores (69.04% for SymNoise, 64.69% for NEFTune) are presented without any accompanying measurements of local curvature, Lipschitz constants, or Hessian-based quantities that would verify the proposed mechanism.

    Authors: We agree that empirical verification of the curvature mechanism would strengthen the claims. The revised version will add a new subsection with measurements of local Lipschitz constants (or equivalent curvature proxies) across the compared methods to directly link performance gains to the proposed regularization effect. revision: yes

  3. Referee: [Abstract] Abstract: no ablation is described that isolates the symmetry of the noise distribution from other potential differences in implementation, hyper-parameters, or random seeds when comparing SymNoise to the NEFTune baseline.

    Authors: The primary experiments control for model architecture, dataset, optimizer, and all hyperparameters, differing only in the noise distribution. To further isolate symmetry, we will add a dedicated ablation experiment that applies symmetric versus non-symmetric variants under identical random seeds and implementation details. revision: yes

Circularity Check

0 steps flagged

No significant circularity; central claims are empirical performance results without load-bearing derivations

full rationale

The paper reports empirical AlpacaEval gains for SymNoise (69.04%) over NEFTune (64.69%) on LLaMA-2-7B with Alpaca and other datasets, attributing superiority to stricter local-curvature regulation by symmetric noise. The abstract references a 'theoretical analysis' only for comparability of uniform vs. Gaussian noise; no equations, curvature bounds, or derivations specific to symmetry are provided in the text. No self-citations, fitted parameters renamed as predictions, or self-definitional steps appear. Results are presented as direct experimental outcomes against external baselines, with no reduction of the claimed mechanism to inputs by construction. This is a standard empirical contribution without circular derivation chains.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Only the abstract is available; no free parameters, axioms, or invented entities can be identified from the provided text.

pith-pipeline@v0.9.0 · 5799 in / 1047 out tokens · 18683 ms · 2026-05-25T04:49:07.354909+00:00 · methodology

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  • Cost.FunctionalEquation washburn_uniqueness_aczel echoes
    ?
    echoes

    ECHOES: this paper passage has the same mathematical shape or conceptual pattern as the Recognition theorem, but is not a direct formal dependency.

    we introduce a new fine-tuning method... utilizing symmetric noise in embeddings. This method aims to enhance the model's function by more stringently regulating its local curvature... Each noise component is generated with an equal probability of 1/2 for the values −1 and 1.

  • Foundation.AlphaCoordinateFixation J_uniquely_calibrated_via_higher_derivative echoes
    ?
    echoes

    ECHOES: this paper passage has the same mathematical shape or conceptual pattern as the Recognition theorem, but is not a direct formal dependency.

    our goal is to have the gradient approach zero in the immediate vicinity of an input altered by a minimal amount... f(x+ε)=f(x−ε)

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Reference graph

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