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..
Generative Modeling Reinvents Supervised Learning: Label Repurposing with Predictive Consistency Learning
Authors: Yang Li, Jiale Ma, Yebin Yang, Qitian Wu, Hongyuan Zha, Junchi Yan
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on vision, text, and graph tasks show the superiority of PCL over conventional supervised training in complex label prediction tasks. (...) 5. Experiments (...) Table 1. Prediction error ( 10 2) of SL and PCL on top of graph models on various types of N-body simulation systems. (...) Table 3. Results on Semantic Segmentation. (...) Table 4. Evaluation on LLM fine-tuning. |
| Researcher Affiliation | Academia | 1School of Artificial Intelligence, Shanghai Jiao Tong University 2Shanghai Innovation Institute 3Broad Institute of MIT and Harvard 4The Chinese University of Hong Kong, Shenzhen. |
| Pseudocode | Yes | Algorithm 1 Predictive Consistency Training (...) Algorithm 2 Multistep Prediction |
| Open Source Code | Yes | Code available at github repository. |
| Open Datasets | Yes | We utilize the ADE20K dataset (Zhou et al., 2019) (...) The Alpaca (Taori et al., 2023) dataset is based on the self-instruct method (Wang et al., 2022) |
| Dataset Splits | Yes | We collect 5000 trajectories for training, 2000 for validation, and 2000 for testing for each configuration. |
| Hardware Specification | Yes | Experiments for constrained n-body simulation are conducted on a single GPU of NVIDIA RTX 4090. For semantic segmentation, a single NVIDIA H100 GPU was employed, and experiments for next-token prediction are performed on 8 GPUs of NVIDIA H800. |
| Software Dependencies | No | The paper does not explicitly provide specific software names with version numbers. |
| Experiment Setup | Yes | LPCL(θ) =E λ1d fθ(x, yt, t), y + λ1d fθ(x, yt , t ), y) + λ2d fθ(x, yt, t), fθ(x, yt , t ) (...) we adopt 4 graph neural layers (...) with a maximum noise step of 1000 and 5 iterations, the time steps t are set as [1000, 800, 600, 400, 200], ensuring a gradual reduction of noise over the course of iterations. |