mlsanityData to 14 Oct 2026

Layer-Informed Fine-Tuning via Three-Stage Functional Segmentation of LLMs

arXiv 2609.38027 ↗First posted 28 Sep 2026cs.CLcited by 0

On the map: LLM reasoning → Reasoning benchmarks and evaluation also fits: Test-time compute and efficient thinking

Abstract (arXiv)

In recent years, the performance of large language models (LLMs) on reasoning tasks has been remarkable, even surpassing human capabilities on various benchmarks. However, there remains a lack of clear understanding in the academic community regarding how the structure and internal parameters of LLMs progressively solve complex reasoning problems. In this study, we investigate the inference process of LLMs on cross-linguistic materials and propose the hypothesis that LLM layers exhibit a structured division of labor across conceptualization, reasoning, and textualization. Based on this hypothesis, we introduce a bottleneck identification mechanism using sensitivity analysis to pinpoint the most critical functional stage for a specific task. Leveraging this insight, we propose a novel approach, Layer-Informed Fine-Tuning (LIFT), which achieves efficient and effective fine-tuning by selectively updating only these functionally critical layers. We then conduct extensive experiments to show that the LIFT method not only accelerates the training process but also significantly improves model performance.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 37 of its 53 references that are in the map. First posted on arXiv 28 Sep 2026.

Builds on

37 of 53 references.

Closest ancestors · 5

Same direction, newest first

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  2. 2024Racing Thoughts: Explaining Contextualization Errors in Large Language Models2 Oct
  3. 2024Beyond English-Centric LLMs: What Language Do Multilingual Language Models Think in?20 Aug
  4. 2023Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning20 May
  5. 2022FOLIO: Natural Language Reasoning with First-Order Logic2 Sep

Other references · 32

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  4. 2024ShortGPT: Layers in Large Language Models are More Redundant Than You Expectcited by 383
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  6. 2025Layer by Layer: Uncovering Hidden Representations in Language Modelscited by 311
  7. 2023Function Vectors in Large Language Modelscited by 308
  8. 2024Do Llamas Work in English? On the Latent Language of Multilingual Transformerscited by 281
  9. 2024The Unreasonable Ineffectiveness of the Deeper Layerscited by 226
  10. 2021All Bark and No Bite: Rogue Dimensions in Transformer Language Models Obscure Representational Qualitycited by 156
  11. 2024How do Large Language Models Handle Multilingualism?cited by 150
  12. 2024The Remarkable Robustness of LLMs: Stages of Inference?cited by 146
  13. 2024Safety Layers in Aligned Large Language Models: The Key to LLM Securitycited by 134
  14. 2024Not All Layers of LLMs Are Necessary During Inferencecited by 96
  15. 2024The Semantic Hub Hypothesis: Language Models Share Semantic Representations Across Languages and Modalitiescited by 69
  16. 2024Emergence of a High-Dimensional Abstraction Phase in Language Transformerscited by 56
  17. 2024Transformer Layers as Painterscited by 54
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  19. 2025Paths Not Taken: Understanding and Mending the Multilingual Factual Recall Pipelinecited by 21
  20. 2025How Do Language Models Compose Functions?cited by 8

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