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arxiv: 2605.28042 · v1 · pith:PFJRCOLJnew · submitted 2026-05-27 · 💻 cs.CL · cs.AI· cs.LG

Extracting Small Translation Specialists from LLMs by Aggressively Pruning Experts

Liu O. Martin , Lucas Bandarkar , Nanyun Peng This is my paper

Pith reviewed 2026-06-29 12:45 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.LG
keywords mixture of expertsmodel pruningmachine translationlarge language modelsmodel compressionexpert specializationmultilingual capabilities
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The pith

Mixture-of-experts LLMs yield compact translation specialists by pruning up to 90 percent of experts with little quality loss.

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

The paper shows that large language models built with mixture-of-experts layers contain many experts whose capabilities are specialized and separable. Experts that do not contribute to machine translation can be identified and removed from the model. Without any retraining, half the experts can be pruned while translation quality stays nearly the same, and 70 percent can be removed with only small losses. A short round of supervised fine-tuning then allows 75 percent pruning to match the original performance, and in some cases nearly 90 percent while still producing reasonable translations. The approach works because the largest parameter blocks in these models are modular, so most of them turn out to be unnecessary for this single task.

Core claim

The central claim is that expert specialization and the separability of multilingual capabilities let us identify experts irrelevant to translation and prune them from mixture-of-experts LLMs. Without retraining, 50 percent of experts can be removed with negligible degradation and 70 percent with only minor losses. A very short supervised fine-tuning step recovers baseline performance after pruning 75 percent of experts, and in some settings nearly 90 percent can be removed while keeping reasonable translation quality. The result is that translation requires only a fraction of the model, enabling substantial compression of the MoE blocks that hold over 90 percent of the parameters.

What carries the argument

The pruning procedure that locates translation-irrelevant experts through their specialization patterns and removes them from the mixture-of-experts layers.

If this is right

  • Half the experts can be pruned with no retraining and negligible degradation in translation quality.
  • Seventy percent pruning produces only minor losses without retraining.
  • A brief supervised fine-tuning step recovers full baseline performance after 75 percent of experts are removed.
  • In some settings nearly 90 percent of experts can be pruned while translation quality remains reasonable.

Where Pith is reading between the lines

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

  • The same identification step could be applied to isolate specialists for other tasks such as code generation or summarization.
  • The separability observed here implies that multilingual performance is concentrated in a small subset of experts rather than distributed evenly.
  • Models could be pretrained with explicit task routing to make such aggressive pruning even more reliable in the future.
  • Deployment of translation systems could move to much smaller hardware once the irrelevant experts are stripped out.

Load-bearing premise

Experts are specialized enough that some can be identified as irrelevant to translation and removed without degrading quality.

What would settle it

A large drop in translation accuracy across multiple language pairs after the identified experts are pruned, even following the short fine-tuning step.

Figures

Figures reproduced from arXiv: 2605.28042 by Liu O. Martin, Lucas Bandarkar, Nanyun Peng.

Figure 1
Figure 1. Figure 1: We isolate a narrow backbone of experts useful for machine translation and prune the rest without performance degradation. Above, we display an example of pruning 69% of experts of 24-layer GPT-OSS-20B, with the top panel displaying the original expert indices. is especially limiting in resource-constrained set￾tings such as mobile and embedded devices (Gaido et al., 2025). This inefficiency stems from the… view at source ↗
Figure 2
Figure 2. Figure 2: Curves displaying the performance degradation of GPT-OSS as more experts are pruned for our method [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: FLoRes and out-of-domain pruning curves for GPT-OSS and Qwen3-30B-A3B. Each panel compares English→𝑋 translation on FLoRes alongside a domain-specific dataset. The curves on the out-of-domain datasets broadly mirror the FLoRes trends, demonstrating the pruned model’s generalization to other translation domains. as parameter-efficient methods are insufficient to recover entire pruned parameter blocks. 5 Res… view at source ↗
Figure 4
Figure 4. Figure 4: Multilingual generalization of the pruned models to languages unseen during calibration. The blue curves [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Full GPT-OSS per-language ablation curves for English [PITH_FULL_IMAGE:figures/full_fig_p014_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Full GPT-OSS per-language ablation curves for [PITH_FULL_IMAGE:figures/full_fig_p015_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: GPT-OSS German diagnostic isolating the effect of dynamic capacity allocation under a fixed routing-mass [PITH_FULL_IMAGE:figures/full_fig_p016_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: GPT-OSS inversion controls for expert ordering and layerwise retained-capacity allocation. Both panels [PITH_FULL_IMAGE:figures/full_fig_p016_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Out-of-domain generalization for 𝑋→English translation. This figure is the reverse-direction counterpart to the main-paper English→𝑋 out-of-domain evaluation and reports (Routing-mass, Dynamic) pruning curves for GPT-OSS and Qwen3-30B-A3B. Each panel compares FLoRes with a domain-specific dataset for the corresponding source language: ArzEn-MultiGenre for Egyptian Arabic, BanglaSTEM for Bengali, JRC-Acquis… view at source ↗
Figure 10
Figure 10. Figure 10: Aggregate Qwen3-30B-A3B pruning ablations for English [PITH_FULL_IMAGE:figures/full_fig_p018_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Per-language Qwen3-30B-A3B pruning ablation curves for English [PITH_FULL_IMAGE:figures/full_fig_p018_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Cross-language transfer of GPT-OSS language-specific pruning configurations. Each panel evaluates one [PITH_FULL_IMAGE:figures/full_fig_p019_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: GPT-OSS comparison of multilingual and single-language calibration on the four core languages. Rows [PITH_FULL_IMAGE:figures/full_fig_p020_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: GPT-OSS direction-transfer comparison for multilingual pruning configurations. Each panel evaluates [PITH_FULL_IMAGE:figures/full_fig_p021_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Direction transfer for single-language pruning configurations on the four core languages. Columns [PITH_FULL_IMAGE:figures/full_fig_p022_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Direction transfer for single-language pruning configurations on out-of-domain [PITH_FULL_IMAGE:figures/full_fig_p023_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: GPT-OSS English→𝑋 translation with a single shared multilingual (Routing-mass, Dynamic) pruning configuration. The configuration is calibrated using data aggregated over the four core English→𝑋 language directions and evaluated on seven target languages: the four core languages and three languages unseen during calibration. Scores are xCOMET, plotted as a function of the percentage of experts dropped per … view at source ↗
Figure 18
Figure 18. Figure 18: GPT-OSS 𝑋→English translation with a single shared multilingual (Routing-mass, Dynamic) pruning configuration. The configuration is calibrated using data aggregated over the four core 𝑋→English language directions and evaluated on seven source languages: the four core languages and three languages unseen during calibration. Scores are xCOMET, plotted as a function of the percentage of experts dropped per … view at source ↗
Figure 19
Figure 19. Figure 19: Retained-expert overlap across language-specific forward masks. We compute global IoU over retained [PITH_FULL_IMAGE:figures/full_fig_p027_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Excess retained-set IoU over a per-layer random-retention baseline. Rows correspond to pruning levels [PITH_FULL_IMAGE:figures/full_fig_p028_20.png] view at source ↗
Figure 21
Figure 21. Figure 21: Layerwise retained-set intersections between language-specific forward masks at [PITH_FULL_IMAGE:figures/full_fig_p029_21.png] view at source ↗
read the original abstract

Modern large language models (LLMs) achieve state-of-the-art machine translation performance, but they do so as broad generalists largely trained for many tasks and capabilities unrelated to translation. Thus, they are heavily overparameterized for this task, resulting in excessive memory and compute requirements. In this paper, we present a method for aggressively pruning experts from modern mixture-of-experts LLMs while incurring negligible degradation in translation quality. Our approach exploits expert specialization and the separability of multilingual capabilities in LLMs to identify experts irrelevant to translation. And because of the modular nature of MoEs, these can be easily pruned without any training. Without retraining, we are able to prune half of all experts with negligible degradation and 70% with only minor losses. With a very short SFT, we prune 75% of experts while recovering baseline performance, and in some settings remove nearly 90% while maintaining reasonable translation quality. Overall, our results show that translation requires only a fraction of the LLM, enabling substantial compression of the MoE blocks that contain over 90% of parameters.

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

2 major / 1 minor

Summary. The paper claims that modern MoE LLMs are overparameterized for machine translation and presents a pruning method that exploits expert specialization and separability of multilingual capabilities to identify and remove translation-irrelevant experts. Without retraining, half the experts can be pruned with negligible degradation and 70% with only minor losses; a short SFT allows 75% pruning while recovering baseline performance and up to nearly 90% in some settings while maintaining reasonable quality. The results indicate that translation requires only a small fraction of the parameters in the MoE blocks.

Significance. If the empirical outcomes are reproducible, the work shows that task-specific capabilities in large MoE models can be isolated to a small expert subset, enabling substantial compression without full retraining. The modular pruning approach without training is a clear strength, as is the demonstration that high pruning ratios are achievable while preserving translation quality. This has direct implications for efficient deployment of specialized MoE models.

major comments (2)
  1. [Abstract] Abstract: the central quantitative claims (pruning 50% of experts with negligible degradation, 70% with minor losses, 75% with short SFT recovering baseline) are reported without any description of the expert identification procedure, the specific MoE models used as baselines, the translation datasets, or statistical measures such as error bars, rendering the soundness of the separability assumption and the pruning results impossible to evaluate.
  2. [Method] Method section (implied by the pruning procedure): the assumption that expert specialization permits reliable identification of translation-irrelevant experts is presented as the enabling mechanism, yet no concrete algorithm, scoring function, or validation experiment for this identification step is supplied; this is load-bearing for all reported pruning ratios.
minor comments (1)
  1. Add a dedicated reproducibility subsection or appendix listing all models, datasets, pruning thresholds, and evaluation metrics so that the reported compression ratios can be independently verified.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the careful review and constructive feedback. We agree that additional details are needed for reproducibility and evaluation, and we will revise the manuscript accordingly.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central quantitative claims (pruning 50% of experts with negligible degradation, 70% with minor losses, 75% with short SFT recovering baseline) are reported without any description of the expert identification procedure, the specific MoE models used as baselines, the translation datasets, or statistical measures such as error bars, rendering the soundness of the separability assumption and the pruning results impossible to evaluate.

    Authors: We agree that the abstract, in its current form, is too high-level to allow full evaluation of the claims. In the revised manuscript we will expand the abstract to include a concise description of the expert identification procedure, name the specific MoE models used, reference the translation datasets, and note that results are reported with statistical measures such as error bars across multiple runs. revision: yes

  2. Referee: [Method] Method section (implied by the pruning procedure): the assumption that expert specialization permits reliable identification of translation-irrelevant experts is presented as the enabling mechanism, yet no concrete algorithm, scoring function, or validation experiment for this identification step is supplied; this is load-bearing for all reported pruning ratios.

    Authors: We acknowledge that the manuscript does not supply a concrete algorithm, scoring function, or validation experiments for identifying translation-irrelevant experts. We will revise the method section to provide a detailed description of the algorithm, the exact scoring function employed, and dedicated validation experiments that demonstrate the separability assumption and the reliability of the identification step. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical results are self-contained

full rationale

The paper describes an empirical pruning procedure on MoE LLMs that identifies translation-irrelevant experts via observed specialization and separability, then reports direct experimental outcomes (pruning ratios and quality metrics) without equations, fitted parameters, or derivations that reduce to inputs by construction. No self-citation chains, ansatzes, or uniqueness theorems are invoked as load-bearing steps; the separability premise is presented as an observed property of the evaluated models rather than a derived or self-referential claim. The central results therefore stand as independent experimental findings.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The approach rests on the domain assumption that experts in MoE LLMs are sufficiently specialized and that multilingual capabilities are separable enough to allow pruning without retraining. No free parameters or invented entities are mentioned in the abstract.

axioms (1)
  • domain assumption Mixture-of-experts LLMs contain experts with task-specific specialization that can be identified and removed for a target task such as translation.
    This premise is invoked to justify pruning without training and is the basis for the reported compression ratios.

pith-pipeline@v0.9.1-grok · 5728 in / 1275 out tokens · 28748 ms · 2026-06-29T12:45:17.771404+00:00 · methodology

discussion (0)

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Forward citations

Cited by 1 Pith paper

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

  1. How Modular Is a Frontier Mixture-of-Experts? A Pre-registered Causal Test in Which Apparent Expert Modularity Mostly Dissolves

    cs.LG 2026-06 conditional novelty 8.0

    Pre-registered ablation tests on Command A+ reveal that only one of six expert families (Arabic) shows clean selective modularity; all others fail selectivity or are measurement-dependent.

Reference graph

Works this paper leans on

7 extracted references · 4 canonical work pages · cited by 1 Pith paper · 2 internal anchors

  1. [1]

    InProceedings of the Sixth Conference on Machine Translation, pages 775–780, Online

    Efficient machine translation with model prun- ing and quantization. InProceedings of the Sixth Conference on Machine Translation, pages 775–780, Online. Association for Computational Linguistics. MaximilianaBehnkeandKennethHeafield.2021. Prun- ing neural machine translation for speed using group lasso. InProceedings of the Sixth Conference on Machine Tra...

    2021

  2. [2]

    InFindings of the Association for Computational Linguistics: NAACL 2024, pages 287–301, Mexico City, Mexico

    Examining modularity in multilingual LMs via language-specialized subnetworks. InFindings of the Association for Computational Linguistics: NAACL 2024, pages 287–301, Mexico City, Mexico. Association for Computational Linguistics. Tim Dettmers, Mike Lewis, Younes Belkada, and Luke Zettlemoyer. 2022. GPT3.int8(): 8-bit matrix mul- tiplication for transform...

    2024

  3. [3]

    Jay Gala, Pranjal A

    Translategemma technical report.Preprint, arXiv:2601.09012. Jonathan Frankle, Gintare Karolina Dziugaite, Daniel Roy, and Michael Carbin. 2021. Pruning neural networks at initialization: Why are we missing the mark? InInternational Conference on Learning Representations. Marco Gaido, Roman Grundkiewicz, Thamme Gowda, and Matteo Negri. 2025. Findings of th...

    work page arXiv 2021

  4. [4]

    Shwai He, Daize Dong, Liang Ding, and Ang Li

    Banglastem: A parallel corpus for techni- cal domain bangla-english translation.Preprint, arXiv:2511.03498. Shwai He, Daize Dong, Liang Ding, and Ang Li. 2025. Towards efficient mixture of experts: A holistic study ofcompressiontechniques.TransactionsonMachine Learning Research. Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015. Distillingtheknowledgein...

    work page arXiv 2025

  5. [5]

    gpt-oss-120b & gpt-oss-20b Model Card

    Not all experts are equal: Efficient expert pruning and skipping for mixture-of-experts large language models. InProceedings of the 62nd Annual Meeting of the Association for Computational Lin- guistics (Volume 1: Long Papers), pages 6159–6172, Bangkok, Thailand. Association for Computational Linguistics. Yasmin Moslem, Muhammad Hazim Al Farouq, and John ...

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  6. [6]

    Transactions of the Association for Computational Linguistics, 13:73–95

    Salutetheclassic: Revisitingchallengesofma- chine translation in the age of large language models. Transactions of the Association for Computational Linguistics, 13:73–95. David Ponce, Harritxu Gete, and Thierry Etchegoyhen

  7. [7]

    No Language Left Behind: Scaling Human-Centered Machine Translation

    Vicomtech@WMT 2025: Evolutionary model compression for machine translation. InProceedings of the Tenth Conference on Machine Translation, pages 1011–1021, Suzhou, China. Association for Computational Linguistics. ZihanQiu,ZeyuHuang,BoZheng,KaiyueWen,Zekun Wang, Rui Men, Ivan Titov, Dayiheng Liu, Jingren Zhou, and Junyang Lin. 2025. Demons in the detail: O...

    work page internal anchor Pith review Pith/arXiv arXiv 2025