Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Guided Trajectory Generation with Diffusion Models for Offline Model-based Optimization
Authors: Taeyoung Yun, Sujin Yun, Jaewoo Lee, Jinkyoo Park
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiment results demonstrate that our method outperforms competitive baselines on Design-Bench and its practical variants. |
| Researcher Affiliation | Collaboration | Taeyoung Yun1 Sujin Yun1 Jaewoo Lee1 Jinkyoo Park1,2 1Korea Advanced Institute of Science and Technology (KAIST) 2Omelet.ai |
| Pseudocode | Yes | Algorithm 1 Trajectory construction procedure of GTG |
| Open Source Code | Yes | The code is publicly available in https://github.com/dbsxodud-11/GTG. |
| Open Datasets | Yes | We empirically demonstrate that our method achieves superior performance on Design-Bench, a well-known benchmark for MBO with a variety of real-world tasks. ... Design-Bench [5] is the most widely used benchmark for evaluating MBO algorithms. |
| Dataset Splits | No | The paper mentions training models on the offline dataset and using sparse/noisy variants for evaluation but does not explicitly provide training, validation, or test dataset splits with percentages or sample counts. |
| Hardware Specification | Yes | All training is done with a single NVIDIA RTX 3090 GPU and takes approximately 30 minutes for discrete tasks and 2 hours for continuous tasks. |
| Software Dependencies | No | The paper mentions using "Adam optimizer [40]" and "temporal U-Net architecture from Diffuser [18]" but does not specify version numbers for these or other software libraries/frameworks like Python or PyTorch. |
| Experiment Setup | Yes | The hyperparameters we used for modeling and training are listed in Table 10. (Hyperparameters for Training Diffusion Models) and Table 11. (Hyperparameters for Training Proxy). |