Reconciling Contradictory Views on the Effectiveness of SFT in LLMs: An Interaction Perspective
The paper investigates the effectiveness of supervised fine-tuning (SFT) in large language models (LLMs) compared to small-scale deep neural networks. It highlights that while SFT can remove noise-like interactions, it often fails to acquire reliable new interactions, leading to inconsistent results. The authors provide insights into the evolution of interactions during SFT and offer practical guidance for LLM training.
- ▪Supervised fine-tuning (SFT) is effective for small-scale deep neural networks but inconsistent for large language models (LLMs).
- ▪The study finds that SFT primarily removes noise-like interactions and rarely acquires reliable new ones.
- ▪The evolution of interactions during SFT can explain the inconsistent effectiveness observed in LLMs.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.17967 |
| Publication time | Tue, 19 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-19T04:04:57.272Z |
| Last seen | 2026-05-19T04:04:57.272Z |
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Computer Science > Artificial Intelligence arXiv:2605.17967 (cs) [Submitted on 18 May 2026] Title:Reconciling Contradictory Views on the Effectiveness of SFT in LLMs: An Interaction Perspective Authors:Junpeng Zhang, Lei Cheng, Guoxi Zhang, Hua Cai, Qing Xu, Quanshi Zhang View a PDF of the paper titled Reconciling Contradictory Views on the Effectiveness of SFT in LLMs: An Interaction Perspective, by Junpeng Zhang and 5 other authors View PDF HTML (experimental) Abstract:This paper explores a scientific question in supervised fine-tuning (SFT): why SFT is broadly effective for small-scale deep neural networks, yet can produce inconsistent or even detrimental effects when applied to large language models (LLMs).
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