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        <title>Memory-Efficient Structured Backpropagation for On-Device LLM Fine-Tuning</title>
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    <abstract>On-device fine-tuning enables privacy-preserving personalization of large language models, but mobile devices impose severe memory constraints, typically 6–12GB shared across all workloads. Existing approaches force a trade-off between exact gradients with high memory (MeBP) and low memory with noisy estimates (MeZO). We propose Memory-efficient Structured Backpropagation (MeSP), which bridges this gap by manually deriving backward passes that exploit LoRA’s low-rank structure. Our key insight is that the intermediate projection h = xA can be recomputed during backward at minimal cost since rank r łl d_in, eliminating the need to store it. MeSP achieves 49% average memory reduction compared to MeBP on Qwen2.5 models (0.5B–3B) while computing mathematically identical gradients. Our analysis also reveals that MeZO’s gradient estimates show near-zero correlation with true gradients (cosine similarity \approx0.001), explaining its slow convergence. MeSP reduces peak memory from 361MB to 136MB for Qwen2.5-0.5B, enabling fine-tuning scenarios previously infeasible on memory-constrained devices.</abstract>
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%0 Conference Proceedings
%T Memory-Efficient Structured Backpropagation for On-Device LLM Fine-Tuning
%A Park, JuneYoung
%A Hong, Yuri
%A Kim, Seongwan
%A Lee, Jaeho
%Y Li, Yunyao
%Y Rehm, Georg
%Y Tu, Mei
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-394-4
%F park-etal-2026-memory
%X On-device fine-tuning enables privacy-preserving personalization of large language models, but mobile devices impose severe memory constraints, typically 6–12GB shared across all workloads. Existing approaches force a trade-off between exact gradients with high memory (MeBP) and low memory with noisy estimates (MeZO). We propose Memory-efficient Structured Backpropagation (MeSP), which bridges this gap by manually deriving backward passes that exploit LoRA’s low-rank structure. Our key insight is that the intermediate projection h = xA can be recomputed during backward at minimal cost since rank r łl d_in, eliminating the need to store it. MeSP achieves 49% average memory reduction compared to MeBP on Qwen2.5 models (0.5B–3B) while computing mathematically identical gradients. Our analysis also reveals that MeZO’s gradient estimates show near-zero correlation with true gradients (cosine similarity \approx0.001), explaining its slow convergence. MeSP reduces peak memory from 361MB to 136MB for Qwen2.5-0.5B, enabling fine-tuning scenarios previously infeasible on memory-constrained devices.
%U https://aclanthology.org/2026.acl-industry.62/
%P 906-916

Markdown (Informal)

[Memory-Efficient Structured Backpropagation for On-Device LLM Fine-Tuning](https://aclanthology.org/2026.acl-industry.62/) (Park et al., ACL 2026)

• Memory-Efficient Structured Backpropagation for On-Device LLM Fine-Tuning (Park et al., ACL 2026)

ACL

• JuneYoung Park, Yuri Hong, Seongwan Kim, and Jaeho Lee. 2026. Memory-Efficient Structured Backpropagation for On-Device LLM Fine-Tuning. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), pages 906–916, San Diego, California, USA. Association for Computational Linguistics.