arxiv: 2604.19520 · v1 · submitted 2026-04-21 · 💻 cs.AI

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SimDiff: Depth Pruning via Similarity and Difference

Yuli Chen , Shuhao Zhang , Fanshen Meng , Bo Cheng , Jiale Han , Qiang Tong , Xiulei Liu

Authors on Pith no claims yet

Pith reviewed 2026-05-10 01:56 UTC · model grok-4.3

classification 💻 cs.AI

keywords depth pruningLLM compressionlayer importancesimilarity metricsmodel accelerationinference speeduplarge language modelspruning criteria

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The pith

SimDiff prunes LLM layers more reliably by jointly scoring representational similarity and transformation differences rather than using cosine similarity alone.

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

The paper seeks to replace unreliable single-metric pruning in large language models, where cosine distance alone can produce erratic results or total collapse. It introduces SimDiff to judge layer importance from two separate angles: how close one layer's outputs are to another's, and how much the layer actually changes the data passing through it. These angles are quantified with two metrics, one that highlights outlier corrections and one that tracks average impact. If correct, this dual view would let practitioners remove more layers while keeping most of the model's capability, yielding faster inference and simpler recovery with light fine-tuning.

Core claim

A layer importance criterion that jointly evaluates representational similarity together with transformation difference, using MSSD to flag outlier-driven corrections and MASD to capture average contribution, delivers higher retained performance than cosine-distance baselines across models from 0.5B to 13B parameters and avoids the unpredictable drops seen with single-heuristic methods.

What carries the argument

The SimDiff layer importance score, which combines a similarity view with two orthogonal difference metrics (MSSD and MASD) to rank which layers can be removed.

If this is right

Retains over 91 percent of LLaMA2-7B performance after removing 25 percent of layers.
Delivers up to 1.49 times faster inference on LLaMA3.1-8B after pruning 12 layers.
Works across model scales from 0.5B to 13B parameters without catastrophic failure.
Allows pruned models to regain capability with only minimal additional fine-tuning.

Where Pith is reading between the lines

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

The same dual-metric logic could be applied to prune attention heads or feed-forward blocks inside individual layers rather than whole layers.
It suggests that other compression techniques such as quantization or distillation might also benefit from checking both similarity and change magnitude.
If the method scales, hardware-aware pruning schedules could be built by weighting the two metrics according to target device memory or latency constraints.

Load-bearing premise

That measuring both how similar layers are and how differently they transform inputs will produce a consistently better removal order than cosine distance and will prevent sudden performance collapse.

What would settle it

On an untested model size or architecture, prune at a 25 percent ratio using both SimDiff and a pure cosine baseline; if the cosine version retains equal or higher accuracy after the same recovery fine-tuning, the claim of reliable superiority would be falsified.

Figures

Figures reproduced from arXiv: 2604.19520 by Bo Cheng, Fanshen Meng, Jiale Han, Qiang Tong, Shuhao Zhang, Xiulei Liu, Yuli Chen.

**Figure 2.** Figure 2: Illustration of SimDiff. Layer importance is computed based on both similarity [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗

**Figure 3.** Figure 3: LoRA fine-tuning performance of the LLaMA3.1-8B model pruned by 12 layers. [PITH_FULL_IMAGE:figures/full_fig_p017_3.png] view at source ↗

**Figure 4.** Figure 4: Pruning performance of LLaMA3.1-8B under different [PITH_FULL_IMAGE:figures/full_fig_p018_4.png] view at source ↗

**Figure 5.** Figure 5: WikiText2 perplexity (PPL) for Mistral-7B-v0.3 and LLaMA3.1-8B across vary [PITH_FULL_IMAGE:figures/full_fig_p019_5.png] view at source ↗

read the original abstract

Depth pruning improves the deployment efficiency of large language models (LLMs) by identifying and removing redundant layers. A widely accepted standard for this identification process is to measure the similarity between layers using cosine distance. However, we find that methods relying solely on this one-dimensional heuristic can exhibit unpredictable performance and even catastrophic collapse across different architectures. To address this issue, we propose SimDiff, a novel layer importance criterion that jointly evaluates layers from two orthogonal perspectives: representational similarity and transformation difference. The difference is quantified using two distinct metrics: MSSD, which is sensitive to outliers and identifies layers that make decisive corrections, and MASD, which robustly measures a layer's average contribution. Extensive experiments on multiple models ranging from 0.5B to 13B parameters demonstrate that SimDiff significantly outperforms state-of-the-art baselines across various pruning ratios. Notably, our method retains over 91% of LLaMA2-7B's performance at a 25% pruning ratio and achieves up to a 1.49x inference speedup when pruning 12 layers on LLaMA3.1-8B. We also show that pruned models can be effectively recovered with minimal fine-tuning.

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

SimDiff adds MSSD and MASD to layer pruning but the reported gains lack an ablation isolating those metrics from cosine baselines.

read the letter

The core idea is to replace single cosine similarity for picking redundant LLM layers with a score that also measures transformation differences via MSSD (outlier-sensitive) and MASD (average contribution). The abstract says this avoids the unpredictable drops or collapse seen in prior work, and the numbers look usable: over 91% retention on LLaMA2-7B at 25% pruning plus up to 1.49x speedup on LLaMA3.1-8B after pruning 12 layers, with recovery via light fine-tuning. They test a range of models from 0.5B to 13B, which is better than many pruning papers that stick to one or two sizes.

Referee Report

1 major / 1 minor

Summary. The paper proposes SimDiff, a novel layer importance criterion for depth pruning of LLMs. It jointly evaluates representational similarity (via MSSD, sensitive to outliers) and transformation difference (via MASD, for average contribution) as orthogonal perspectives, arguing that cosine-distance-only methods lead to unpredictable performance or collapse. Experiments on models from 0.5B to 13B parameters claim significant outperformance over SOTA baselines at various pruning ratios, including >91% retention of LLaMA2-7B performance at 25% pruning and up to 1.49x inference speedup on LLaMA3.1-8B after pruning 12 layers, with effective recovery via minimal fine-tuning.

Significance. If the central claims hold, SimDiff offers a more robust pruning heuristic than single-metric baselines, enabling reliable efficiency gains in LLM deployment with reduced risk of performance collapse. The dual-metric construction and reported speedups/accuracy retention would be a practical contribution to model compression literature.

major comments (1)

[Experiments] The central claim that jointly using MSSD and MASD provides a more reliable layer importance criterion than cosine distance alone (avoiding unpredictable performance or collapse) is load-bearing for attributing the reported gains (e.g., 91% retention at 25% pruning on LLaMA2-7B). However, no direct ablation isolating the MSSD/MASD contribution versus a cosine baseline on identical models, pruning ratios, and recovery procedures is presented; without this, gains could stem from implementation details or heuristics rather than the two-metric construction.

minor comments (1)

The manuscript would benefit from expanded details on the full experimental setup, including exact baseline implementations, hyperparameter choices for pruning and recovery, and any potential confounds in the multi-model evaluation.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their detailed and constructive feedback. We address the major experimental concern below and will revise the manuscript to incorporate the suggested ablation, which will strengthen the attribution of our results to the proposed dual-metric criterion.

read point-by-point responses

Referee: [Experiments] The central claim that jointly using MSSD and MASD provides a more reliable layer importance criterion than cosine distance alone (avoiding unpredictable performance or collapse) is load-bearing for attributing the reported gains (e.g., 91% retention at 25% pruning on LLaMA2-7B). However, no direct ablation isolating the MSSD/MASD contribution versus a cosine baseline on identical models, pruning ratios, and recovery procedures is presented; without this, gains could stem from implementation details or heuristics rather than the two-metric construction.

Authors: We agree that a direct ablation isolating the contribution of the joint MSSD/MASD criterion versus a pure cosine-distance baseline, under identical models, pruning ratios, and recovery procedures, would provide clearer evidence. The current manuscript demonstrates that SimDiff outperforms several cosine-based SOTA baselines across multiple models and ratios, and includes observations of collapse with single-metric approaches. However, these comparisons do not hold every variable exactly constant. In the revised manuscript we will add a targeted ablation on LLaMA2-7B at the 25% pruning ratio (and similarly for other reported settings), replacing only the layer-selection criterion with cosine distance while keeping the rest of the pipeline unchanged. This will allow direct quantification of the performance difference attributable to the orthogonal perspectives. revision: yes

Circularity Check

0 steps flagged

No significant circularity; new metrics are independently defined and experimentally validated

full rationale

The paper defines MSSD and MASD as new metrics for representational similarity and transformation difference without reducing them to fitted parameters, self-citations, or prior ansatzes from the same authors. The central claim rests on experimental comparisons across models (0.5B-13B) rather than any derivation that loops back to its own inputs by construction. No equations or sections in the provided text exhibit self-definitional, fitted-prediction, or load-bearing self-citation patterns. The method is presented as an orthogonal extension to cosine distance, with performance gains attributed to empirical results, not definitional equivalence.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The abstract provides insufficient detail to identify specific free parameters, axioms, or invented entities. The new metrics MSSD and MASD are introduced but their exact formulations and any parameters are not described.

pith-pipeline@v0.9.0 · 5520 in / 1220 out tokens · 48663 ms · 2026-05-10T01:56:48.231260+00:00 · methodology

discussion (0)

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Works this paper leans on

32 extracted references · 19 canonical work pages · 3 internal anchors

[1]

Jaiswal, S

A. Jaiswal, S. Liu, T. Chen, Z. Wang, The emergence of essential spar- sity in large pre-trained models: The weights that matter, in: A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, S. Levine (Eds.), Advances in Neural Information Processing Systems 36: Annual Con- ference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA,...

2023
[2]

X. Ma, G. Fang, X. Wang, Llm-pruner: On the structural pruning of large language models, in: A. Oh, T. Naumann, A. Globerson, K. Saenko, M. Hardt, S. Levine (Eds.), Advances in Neural Informa- tion Processing Systems 36: Annual Conference on Neural Information 20 Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, De- cember 10 - 16, 2023, 2023

2023
[3]

X. Men, M. Xu, Q. Zhang, Q. Yuan, B. Wang, H. Lin, Y. Lu, X. Han, W. Chen, Shortgpt: Layers in large language models are more redundant than you expect, in: W. Che, J. Nabende, E. Shutova, M. T. Pilehvar (Eds.), Findings of the Association for Computational Linguistics, ACL 2025, Vienna, Austria, July 27 - August 1, 2025, Association for Com- putational L...

2025
[4]

Frantar, D

E. Frantar, D. Alistarh, Sparsegpt: Massive language models can be accurately pruned in one-shot, in: A. Krause, E. Brunskill, K. Cho, B. Engelhardt, S. Sabato, J. Scarlett (Eds.), International Conference on Machine Learning, ICML 2023, 23-29 July 2023, Honolulu, Hawaii, USA, Vol. 202 of Proceedings of Machine Learning Research, PMLR, 2023, pp. 10323–10337

2023
[6]

J. Song, K. Oh, T. Kim, H. Kim, Y. Kim, J. Kim, SLEB: streamlin- ing llms through redundancy verification and elimination of transformer blocks, in: Forty-first International Conference on Machine Learning, ICML 2024, Vienna, Austria, July 21-27, 2024, OpenReview.net, 2024

2024
[7]

Ashkboos, M

S. Ashkboos, M. L. Croci, M. G. D. Nascimento, T. Hoefler, J. Hensman, Slicegpt: Compresslargelanguagemodelsbydeletingrowsandcolumns, in: The Twelfth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024, OpenReview.net, 2024

2024
[8]

LLaMA: Open and Efficient Foundation Language Models

H. Touvron, T. Lavril, G. Izacard, X. Martinet, M. Lachaux, T. Lacroix, B. Rozière, N. Goyal, E. Hambro, F. Azhar, A. Rodriguez, A. Joulin, E. Grave, G. Lample, Llama: Open and efficient founda- tion language models, CoRR abs/2302.13971 (2023). arXiv:2302.13971, 21 doi:10.48550/ARXIV.2302.13971. URLhttps://doi.org/10.48550/arXiv.2302.13971

work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2302.13971 2023
[9]

Thakkar, A

H. Thakkar, A. Manimaran, Comprehensive examination of instruction- based language models: A comparative analysis of mistral-7b and llama- 2-7b, in: 2023 International Conference on Emerging Research in Com- putational Science (ICERCS), IEEE, 2023, pp. 1–6

2023
[10]

L. Yin, Y. Wu, Z. Zhang, C. Hsieh, Y. Wang, Y. Jia, G. Li, A. K. Jaiswal, M. Pechenizkiy, Y. Liang, M. Bendersky, Z. Wang, S. Liu, Outlier weighed layerwise sparsity (OWL): A missing secret sauce for pruning llms to high sparsity, in: Forty-first International Conference on Machine Learning, ICML 2024, Vienna, Austria, July 21-27, 2024, OpenReview.net, 2024

2024
[11]

Y. Chen, B. Cheng, J. Han, Y. Zhang, Y. Li, S. Zhang, DLP: dynamic layerwise pruning in large language models, CoRR abs/2505.23807 (2025). arXiv:2505.23807

work page arXiv 2025
[12]

B. Kim, G. Kim, T. Kim, T. Castells, S. Choi, J. Shin, H. Song, Shortened llama: A simple depth pruning for large lan- guage models, CoRR abs/2402.02834 (2024). arXiv:2402.02834, doi:10.48550/ARXIV.2402.02834

work page doi:10.48550/arxiv.2402.02834 2024
[13]

L. Yang, Y. Xu, J. Tan, D. Sahoo, S. Savarese, C. Xiong, H. Wang, S. Heinecke, Entropy-based block pruning for efficient large lan- guage models, CoRR abs/2504.03794 (2025). arXiv:2504.03794, doi:10.48550/ARXIV.2504.03794

work page doi:10.48550/arxiv.2504.03794 2025
[14]

Z. Ling, Z. Li, P. Romero, L. Han, G. Nenadic, Beemanc at the PLABA track of TAC-2024: roberta for task 1 - llama3.1 and gpt-4o for task 2, CoRR abs/2411.07381 (2024). arXiv:2411.07381, doi:10.48550/ARXIV.2411.07381

work page doi:10.48550/arxiv.2411.07381 2024
[15]

Qwen2 Technical Report

Q. Team, et al., Qwen2 technical report, arXiv preprint arXiv:2407.10671 2 (3) (2024)

work page internal anchor Pith review arXiv 2024
[16]

Contributors, Opencompass: A universal evaluation platform for foundation models,https://github.com/open-compass/opencompass (2023)

O. Contributors, Opencompass: A universal evaluation platform for foundation models,https://github.com/open-compass/opencompass (2023). 22

2023
[17]

H. Li, Y. Zhang, F. Koto, Y. Yang, H. Zhao, Y. Gong, N. Duan, T. Bald- win, CMMLU: measuring massive multitask language understanding in chinese, in: L. Ku, A. Martins, V. Srikumar (Eds.), Findings of the As- sociation for Computational Linguistics, ACL 2024, Bangkok, Thailand and virtual meeting, August 11-16, 2024, Association for Computational Linguist...

work page doi:10.18653/v1/2024.findings- 2024
[18]

HellaSwag: Can a Machine Really Finish Your Sentence?

R. Zellers, A. Holtzman, Y. Bisk, A. Farhadi, Y. Choi, Hellaswag: Can a machine really finish your sentence?, arXiv preprint arXiv:1905.07830 (2019)

work page internal anchor Pith review arXiv 1905
[19]

Y. Bisk, R. Zellers, R. L. Bras, J. Gao, Y. Choi, PIQA: reasoning about physicalcommonsenseinnaturallanguage, in: TheThirty-FourthAAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second In- novative Applications of Artificial Intelligence Conference, IAAI 2020, The Tenth AAAI Symposium on Educational Advances in Artificial In- telligence,...

work page doi:10.1609/aaai.v34i05.6239 2020
[20]

H. J. Levesque, E. Davis, L. Morgenstern, The winograd schema chal- lenge., KR 2012 (13th) (2012) 3

2012
[21]

Reddy, D

S. Reddy, D. Chen, C. D. Manning, Coqa: A conversational question answering challenge, Trans. Assoc. Comput. Linguistics 7 (2019) 249–

2019
[22]

, year =

doi:10.1162/TACL_A_00266. URLhttps://doi.org/10.1162/tacl_a_00266

work page doi:10.1162/tacl_a_00266
[23]

BoolQ: Exploring the Surprising Difficulty of Natural Yes/No Questions

C. Clark, K. Lee, M. Chang, T. Kwiatkowski, M. Collins, K. Toutanova, Boolq: Exploringthesurprisingdifficultyofnaturalyes/noquestions, in: J. Burstein, C. Doran, T. Solorio (Eds.), Proceedings of the 2019 Con- ference of the North American Chapter of the Association for Computa- tional Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis,...

work page doi:10.18653/v1/n19-1300 2019
[24]

G. Lai, Q. Xie, H. Liu, Y. Yang, E. H. Hovy, RACE: large-scale read- ing comprehension dataset from examinations, in: M. Palmer, R. Hwa, S. Riedel (Eds.), Proceedings of the 2017 Conference on Empirical Meth- ods in Natural Language Processing, EMNLP 2017, Copenhagen, Den- mark, September 9-11, 2017, Association for Computational Linguistics, 2017, pp. 78...

work page doi:10.18653/v1/d17-1082 2017
[25]

Generate & rank: A multi-task framework for math word problems

T. Hasan, A. Bhattacharjee, M. S. Islam, K. S. Mubasshir, Y. Li, Y. Kang, M. S. Rahman, R. Shahriyar, Xl-sum: Large-scale multilin- gual abstractive summarization for 44 languages, in: C. Zong, F. Xia, W. Li, R. Navigli (Eds.), Findings of the Association for Computational Linguistics: ACL/IJCNLP 2021, Online Event, August 1-6, 2021, Vol. ACL/IJCNLP 2021 ...

work page doi:10.18653/v1/2021.findings- 2021
[26]

K. Sun, D. Yu, D. Yu, C. Cardie, Investigating prior knowledge for chal- lenging chinese machine reading comprehension, Trans. Assoc. Comput. Linguistics 8 (2020) 141–155. doi:10.1162/TACL_A_00305. URLhttps://doi.org/10.1162/tacl_a_00305

work page doi:10.1162/tacl_a_00305 2020
[27]

L. Gao, J. Tow, B. Abbasi, S. Biderman, S. Black, A. DiPofi, C. Foster, L. Golding, J. Hsu, A. Le Noac’h, H. Li, K. McDonell, N. Muennighoff, C. Ociepa, J. Phang, L. Reynolds, H. Schoelkopf, A. Skowron, L. Sutawika, E. Tang, A. Thite, B. Wang, K. Wang, A. Zou, A framework for few-shot language model evaluation (07 2024). doi:10.5281/zenodo.12608602. URLht...

work page doi:10.5281/zenodo.12608602 2024
[28]

A. Wang, A. Singh, J. Michael, F. Hill, O. Levy, S. R. Bowman, GLUE: A multi-task benchmark and analysis platform for natural language un- derstanding, in: 7th International Conference on Learning Representa- tions, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019, 2019

2019
[29]

Proceedings of the AAAI Conference on Artificial Intelligence , author=

K. Sakaguchi, R. L. Bras, C. Bhagavatula, Y. Choi, Winogrande: An adversarial winograd schema challenge at scale, in: The Thirty- Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, The Thirty-Second Innovative Applications of Artificial Intelligence 24 Conference, IAAI 2020, The Tenth AAAI Symposium on Educa- tional Advances in Artificial Intel...

work page doi:10.1609/aaai.v34i05.6399 2020
[30]

A systematic classifica- tion of knowledge, reasoning, and context within the ARC dataset

M. Boratko, H. Padigela, D. Mikkilineni, P. Yuvraj, R. Das, A. Mc- Callum, M. Chang, A. Fokoue-Nkoutche, P. Kapanipathi, N. Mattei, R. Musa, K. Talamadupula, M. Witbrock, A systematic classification of knowledge, reasoning, and context within the ARC dataset, in: E. Choi, M. Seo, D. Chen, R. Jia, J. Berant (Eds.), Proceedings of the Workshop on Machine Re...

work page doi:10.18653/v1/w18-2607 2018
[31]

Amini, S

A. Amini, S. Gabriel, S. Lin, R. Koncel-Kedziorski, Y. Choi, H. Ha- jishirzi, Mathqa: Towards interpretable math word problem solving with operation-based formalisms, in: J. Burstein, C. Doran, T. Solorio (Eds.), Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Tech- nologies...

work page doi:10.18653/v1/n19-1245 2019
[32]

doi: 10.18653/v1/D18-

T. Mihaylov, P. Clark, T. Khot, A. Sabharwal, Can a suit of armor con- duct electricity? A new dataset for open book question answering, in: E. Riloff, D. Chiang, J. Hockenmaier, J. Tsujii (Eds.), Proceedings of the 2018 Conference on Empirical Methods in Natural Language Process- ing, Brussels, Belgium, October 31 - November 4, 2018, Association for Comp...

work page doi:10.18653/v1/d18- 2018
[33]

Merity, C

S. Merity, C. Xiong, J. Bradbury, R. Socher, Pointer sentinel mixture models, in: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Pro- ceedings, OpenReview.net, 2017. 25 Algorithm 1Pseudocode of Ternary Search for Optimal Alpha Input: Evaluation function EvaluatePPL(α)that returns a ...

2017