{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T06:00:03Z","timestamp":1780984803210,"version":"3.54.1"},"reference-count":53,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"}],"funder":[{"DOI":"10.13039\/501100001863","name":"New Energy and Industrial Technology Development Organization","doi-asserted-by":"publisher","award":["JPNP20017"],"award-info":[{"award-number":["JPNP20017"]}],"id":[{"id":"10.13039\/501100001863","id-type":"DOI","asserted-by":"publisher"}]},{"name":"JST SPRING","award":["JPMJSP2124"],"award-info":[{"award-number":["JPMJSP2124"]}]},{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["JP22H00509"],"award-info":[{"award-number":["JP22H00509"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"name":"JST CREST","award":["JPMJCR24R4"],"award-info":[{"award-number":["JPMJCR24R4"]}]},{"name":"the Multidisciplinary Cooperative Research Program in CCS, University of Tsukuba"},{"name":"Fujitsu Limited"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2026]]},"DOI":"10.1109\/access.2026.3699357","type":"journal-article","created":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T20:07:38Z","timestamp":1780430858000},"page":"84922-84942","source":"Crossref","is-referenced-by-count":0,"title":["RRLoRA: Refactorized Low-Rank Adaptation With Learning-Rate Restarts for Efficient Fine-Tuning"],"prefix":"10.1109","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-4392-1226","authenticated-orcid":false,"given":"Mingzhe","family":"Yu","sequence":"first","affiliation":[{"name":"Graduate School of Science and Technology, University of Tsukuba, Tsukuba, Ibaraki, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4714-2164","authenticated-orcid":false,"given":"Osamu","family":"Tatebe","sequence":"additional","affiliation":[{"name":"Center for Computational Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref1","article-title":"LoRA: Low-rank adaptation of large language models","author":"Hu","year":"2021","journal-title":"arXiv:2106.09685"},{"key":"ref2","article-title":"Parameter efficient fine-tuning via explained variance adaptation","author":"Paischer","year":"2024","journal-title":"arXiv:2410.07170"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.52202\/079017-3846"},{"key":"ref4","article-title":"OLoRA: Orthonormal low-rank adaptation of large language models","author":"B\u00fcy\u00fckaky\u00fcz","year":"2024","journal-title":"arXiv:2406.01775"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.52202\/079017-2292"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2025.3529807"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2025.3533701"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.52202\/079017-1741"},{"key":"ref9","first-page":"59641","article-title":"Riemannian preconditioned LoRA for fine-tuning foundation models","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Zhang"},{"key":"ref10","article-title":"LoRA meets riemannion: Muon optimizer for parametrization-independent low-rank adapters","author":"Bogachev","year":"2025","journal-title":"arXiv:2507.12142"},{"key":"ref11","article-title":"SGDR: Stochastic gradient descent with warm restarts","author":"Loshchilov","year":"2016","journal-title":"arXiv:1608.03983"},{"key":"ref12","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2014","journal-title":"arXiv:1412.6980"},{"key":"ref13","volume-title":"Muon: An Optimizer for Hidden Layers in Neural Networks","author":"Jordan","year":"2024"},{"key":"ref14","article-title":"Decoupled weight decay regularization","author":"Loshchilov","year":"2017","journal-title":"arXiv:1711.05101"},{"key":"ref15","article-title":"A rank stabilization scaling factor for fine-tuning with LoRA","author":"Kalajdzievski","year":"2023","journal-title":"arXiv:2312.03732"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2023.emnlp-main.319"},{"key":"ref17","article-title":"PyTorch: An imperative style, high-performance deep learning library","author":"Paszke","year":"2019","journal-title":"arXiv:1912.01703"},{"key":"ref18","first-page":"38","article-title":"Transformers: State-of-the-art natural language processing","volume-title":"Proc. 2020 Conf. Empirical Methods Natural Lang. Process., Syst. Demonstrations","author":"Wolf"},{"key":"ref19","volume-title":"PEFT: State-of-the-Art Parameter-Efficient Fine-Tuning Methods","author":"Mangrulkar","year":"2022"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W18-5446"},{"key":"ref21","article-title":"Deep residual learning for image recognition","author":"He","year":"2015","journal-title":"arXiv:1512.03385"},{"key":"ref22","article-title":"The impact of initialization on LoRA finetuning dynamics","author":"Hayou","year":"2024","journal-title":"arXiv:2406.08447"},{"key":"ref23","article-title":"BoolQ: Exploring the surprising difficulty of natural yes\/no questions","author":"Clark","year":"2019","journal-title":"arXiv:1905.10044"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i05.6239"},{"key":"ref25","first-page":"4463","article-title":"Social IQa: Commonsense reasoning about social interactions","volume-title":"Proc. Conf. Empirical Methods Natural Lang. Process. 9th Int. Joint Conf. Natural Lang. Process. (EMNLP-IJCNLP)","author":"Sap"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1472"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.1145\/3474381"},{"key":"ref28","article-title":"Think you have solved question answering? Try ARC, the AI2 reasoning challenge","author":"Clark","year":"2018","journal-title":"arXiv:1803.05457"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-1260"},{"key":"ref30","article-title":"Gemma: Open models based on Gemini research and technology","author":"Team","year":"2024","journal-title":"arXiv:2403.08295"},{"key":"ref31","article-title":"The llama 3 herd of models","author":"Grattafiori","year":"2024","journal-title":"arXiv:2407.21783"},{"key":"ref32","article-title":"Mistral 7B","author":"Jiang","year":"2023","journal-title":"arXiv:2310.06825"},{"key":"ref33","article-title":"Qwen3 technical report","volume-title":"arXiv:2505.09388","author":"Yang","year":"2025"},{"key":"ref34","doi-asserted-by":"publisher","DOI":"10.48550\/ARXIV.1907.11692"},{"key":"ref35","article-title":"MetaMath: Bootstrap your own mathematical questions for large language models","author":"Yu","year":"2023","journal-title":"arxiv:2309.12284"},{"key":"ref36","article-title":"Training verifiers to solve math word problems","author":"Cobbe","year":"2021","journal-title":"arxiv:2110.14168"},{"key":"ref37","article-title":"Measuring mathematical problem solving with the MATH dataset","author":"Hendrycks","year":"2021","journal-title":"arxiv:2103.03874"},{"key":"ref38","volume-title":"SmolLM3: Smol, Multilingual, Long-Context Reasoner","author":"Bakouch","year":"2025"},{"key":"ref39","article-title":"VQA: Visual question answering","author":"Agrawal","year":"2016","journal-title":"arxiv:1505.00468"},{"key":"ref40","first-page":"2263","article-title":"ChartQA: A benchmark for question answering about charts with visual and logical reasoning","volume-title":"Proc. Findings Assoc. Comput. Linguistics, ACL","author":"Masry"},{"key":"ref41","article-title":"LFM2 technical report","volume-title":"arxiv:2511.23404","author":"Amini","year":"2025"},{"key":"ref42","article-title":"Improved baselines with visual instruction tuning","author":"Liu","year":"2023","journal-title":"arxiv:2310.03744"},{"key":"ref43","article-title":"Ministral 3","author":"Liu","year":"2026","journal-title":"arxiv:2511.23404"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-032-08009-7_6"},{"key":"ref45","first-page":"287","article-title":"An analysis of encoder representations in transformer-based machine translation","volume-title":"Proc. EMNLP Workshop Blackbox NLP, Analyzing Interpreting Neural Netw.","author":"Raganato"},{"key":"ref46","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2020.acl-main.270"},{"key":"ref47","article-title":"Transformer feed-forward layers are key-value memories","author":"Geva","year":"2020","journal-title":"arxiv:2012.14913"},{"key":"ref48","article-title":"Locating and editing factual associations in GPT","author":"Meng","year":"2023","journal-title":"arxiv:2202.05262"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-73004-7_2"},{"key":"ref50","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i5.32567"},{"key":"ref51","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2026.3671790"},{"key":"ref52","volume-title":"\u03b1-LoRA: Effective Fine-Tuning via Base Model Rescaling","author":"Firdoussi","year":"2026"},{"key":"ref53","article-title":"Few-shot parameter-efficient fine-tuning is better and cheaper than in-context learning","author":"Liu","year":"2022","journal-title":"arxiv:2205.05638"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/6287639\/11323511\/11547155.pdf?arnumber=11547155","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T05:30:19Z","timestamp":1780983019000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11547155\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"references-count":53,"URL":"https:\/\/doi.org\/10.1109\/access.2026.3699357","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]}}}