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..
Provable Maximum Entropy Manifold Exploration via Diffusion Models
Authors: Riccardo De Santi, Marin Vlastelica, Ya-Ping Hsieh, Zebang Shen, Niao He, Andreas Krause
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we empirically evaluate our approach on both synthetic and high-dimensional text-to-image diffusion, demonstrating promising results. |
| Researcher Affiliation | Academia | 1ETH Zurich, 8092 Zurich, Switzerland 2ETH AI Center, Zurich, Switzerland. Correspondence to: Riccardo De Santi <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Score-based Maximum Entropy Manifold Exploration (S-MEME) Algorithm 2 LINEARFINETUNINGSOLVER (Implementation based on Adjoint Matching (Domingo-Enrich et al., 2024)) |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code availability for the methodology described. |
| Open Datasets | Yes | For this we utilize the stable diffusion (SD) 1.5 (Rombach et al., 2021) checkpoint pre-trained on the LAION-5B dataset (Schuhmann et al., 2022). |
| Dataset Splits | No | The paper uses pre-trained models and discusses fine-tuning and sampling for evaluation. It mentions pre-training on 10K samples for an illustrative setting, but does not specify dataset splits (e.g., train/test/validation) for reproducing their experiments. |
| Hardware Specification | Yes | We fine-tuned the checkpoint with K = 3 iterations of S-MEME on a single Nvidia H100 GPU for the prompt A creative architecture. |
| Software Dependencies | No | The paper mentions using "stable diffusion (SD) 1.5" but does not specify version numbers for other key software components like programming languages (e.g., Python) or libraries (e.g., PyTorch, TensorFlow). |
| Experiment Setup | Yes | For fine-tuning, in this experiment we ran S-MEME for 6000 gradient steps in total, for K = 1, 2, 3, 4. ... we perform an iteration of Algorithm 2 by first sampling 20 trajectories via DDPM of length 400 that are used for solving the lean adjoint ODE with the reward λ log p T (x) and λ = 0.1. Subsequently we perform 2 stochastic gradient steps by the Adam optimizer with batch size 2048, initialized with learning rate 4 10 4. |