{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/recurrent-diffusion-for-large-scale-parameter","title":"Recurrent Diffusion for Large-Scale Parameter Generation","arxiv_id":"2501.11587","date":"2025-01-20","proceeding":null,"authors":["Kai Wang","Dongwen Tang","Wangbo Zhao","Yang You"],"abstract":"Parameter generation has struggled to scale up for a long time, significantly limiting its range of applications. In this study, we introduce \\textbf{R}ecurrent diffusion for large-scale \\textbf{P}arameter \\textbf{G}eneration, called \\textbf{RPG}. We first divide the trained parameters into non-overlapping parts, after which a recurrent model is proposed to learn their relationships. The recurrent model's outputs, as conditions, are then fed into a diffusion model to generate the neural network parameters. Using only a single GPU, recurrent diffusion enables us to generate popular vision and language models such as ConvNeXt-L and LoRA parameters of LLaMA-7B. Meanwhile, across various architectures and tasks, the generated parameters consistently perform comparable results over trained networks. Notably, our approach also shows the potential to generate models for handling unseen tasks, which largely increases the practicality of parameter generation. Our code is available \\href{https://github.com/NUS-HPC-AI-Lab/Recurrent-Parameter-Generation}{here}.","url_abs":"https://arxiv.org/abs/2501.11587v1","url_pdf":"https://arxiv.org/pdf/2501.11587v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"recurrent-diffusion-for-large-scale-parameter","repo_url":"https://github.com/nus-hpc-ai-lab/recurrent-parameter-generation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":null,"task_name":"GPU"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2501.11587","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.11587"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/nus-hpc-ai-lab/recurrent-parameter-generation","reach":{"status":"ok","spdx":"Apache-2.0"}}],"summary":{"ran":1},"by_repo_kind":{"official":{"samples":1,"ran":1,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"2e3dfa3f8f2aee33","entry":"extract","repo":"nus-hpc-ai-lab/recurrent-parameter-generation","repo_kind":"official","path":"model/diffusion.py","file_url":"https://github.com/nus-hpc-ai-lab/recurrent-parameter-generation/blob/HEAD/model/diffusion.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"2e3dfa3f8f2aee33"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}