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..
Rethinking Teacher-Student Curriculum Learning through the Cooperative Mechanics of Experience
Authors: Manfred Diaz, Liam Paull, Andrea Tacchetti
TMLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through experiments covering supervised learning, reinforcement learning, and classical games, we estimate the cooperative values of experiences and use value-proportional curriculum mechanisms to construct curricula, even in cases where TSCL struggles. |
| Researcher Affiliation | Collaboration | Manfred Diaz EMAIL Mila, University of Montreal Liam Paull EMAIL Mila, University of Montreal Canada CIFAR AI Chair Andrea Tacchetti EMAIL Google Deep Mind |
| Pseudocode | Yes | Algorithm 1 Generalized Teacher-Student Curriculum Learning. |
| Open Source Code | No | Regardless, we plan to release the complete source code of all our experiments. |
| Open Datasets | Yes | For instance, in the MNIST dataset (Le Cun & Cortes, 2010) |
| Dataset Splits | No | For both MNIST and CIFAR10 (Krizhevsky, 2009), we trained a model on the complete dataset (e.g., for 200 epochs), extracted the confusion matrix on validation, and identify the top-k most confused pairs of classes. |
| Hardware Specification | Yes | All models and architectures are implemented with PYTORCH (Paszke et al., 2019), are configured using HYDRA (Yadan, 2019), and fit on a workstation equipped with a 16 GB NVIDIA RTX A4000 GPU, 32 GB of RAM, and 32 CPU cores. |
| Software Dependencies | No | All models and architectures are implemented with PYTORCH (Paszke et al., 2019), are configured using HYDRA (Yadan, 2019), and fit on a workstation equipped with a 16 GB NVIDIA RTX A4000 GPU, 32 GB of RAM, and 32 CPU cores. |
| Experiment Setup | Yes | Specification of the model architecture and hyper-parameters selection are provided in Table 2. |