{"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/adaptdiffuser-diffusion-models-as-adaptive","title":"AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners","arxiv_id":"2302.01877","date":"2023-02-03","proceeding":null,"authors":["Zhixuan Liang","Yao Mu","Mingyu Ding","Fei Ni","Masayoshi Tomizuka","Ping Luo"],"abstract":"Diffusion models have demonstrated their powerful generative capability in many tasks, with great potential to serve as a paradigm for offline reinforcement learning. However, the quality of the diffusion model is limited by the insufficient diversity of training data, which hinders the performance of planning and the generalizability to new tasks. This paper introduces AdaptDiffuser, an evolutionary planning method with diffusion that can self-evolve to improve the diffusion model hence a better planner, not only for seen tasks but can also adapt to unseen tasks. AdaptDiffuser enables the generation of rich synthetic expert data for goal-conditioned tasks using guidance from reward gradients. It then selects high-quality data via a discriminator to finetune the diffusion model, which improves the generalization ability to unseen tasks. Empirical experiments on two benchmark environments and two carefully designed unseen tasks in KUKA industrial robot arm and Maze2D environments demonstrate the effectiveness of AdaptDiffuser. For example, AdaptDiffuser not only outperforms the previous art Diffuser by 20.8% on Maze2D and 7.5% on MuJoCo locomotion, but also adapts better to new tasks, e.g., KUKA pick-and-place, by 27.9% without requiring additional expert data. More visualization results and demo videos could be found on our project page.","url_abs":"https://arxiv.org/abs/2302.01877v2","url_pdf":"https://arxiv.org/pdf/2302.01877v2.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":"adaptdiffuser-diffusion-models-as-adaptive","repo_url":"https://github.com/Liang-ZX/adaptdiffuser","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"diversity","task_name":"Diversity"},{"task_slug":"mujoco","task_name":"MuJoCo"}],"methods":[{"method_slug":"diffusion","method_name":"Diffusion"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2302.01877","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.01877"}},"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/Liang-ZX/adaptdiffuser","reach":null}],"summary":{"ran_draft_wrong":1,"ran_honours":1,"unverified":1},"by_repo_kind":{"official":{"samples":3,"ran":2,"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":"586c4bbaec7c496d","entry":"conditional_noise","repo":"Liang-ZX/adaptdiffuser","repo_kind":"official","path":"diffusion/denoising_diffusion_pytorch_adapt.py","file_url":"https://github.com/Liang-ZX/adaptdiffuser/blob/HEAD/diffusion/denoising_diffusion_pytorch_adapt.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"586c4bbaec7c496d"}},{"code_sha256_prefix":"b59ed49c95c3db63","entry":"cosine_beta_schedule","repo":"Liang-ZX/adaptdiffuser","repo_kind":"official","path":"diffusion/denoising_diffusion_pytorch_adapt.py","file_url":"https://github.com/Liang-ZX/adaptdiffuser/blob/HEAD/diffusion/denoising_diffusion_pytorch_adapt.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"b59ed49c95c3db63"}},{"code_sha256_prefix":"475e34e2d9cbe614","entry":"GaussianDiffusion","repo":"Liang-ZX/adaptdiffuser","repo_kind":"official","path":"diffusion/denoising_diffusion_pytorch_adapt.py","file_url":"https://github.com/Liang-ZX/adaptdiffuser/blob/HEAD/diffusion/denoising_diffusion_pytorch_adapt.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"475e34e2d9cbe614"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}