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..
Conflict-Averse Gradient Descent for Multi-task learning
Authors: Bo Liu, Xingchao Liu, Xiaojie Jin, Peter Stone, Qiang Liu
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On a series of challenging multi-task supervised learning and reinforcement learning tasks, CAGrad achieves improved performance over prior state-of-the-art multi-objective gradient manipulation methods. Code is available at https://github.com/Cranial-XIX/CAGrad. |
| Researcher Affiliation | Collaboration | Bo Liu, Xingchao Liu, Xiaojie Jin, , Peter Stone, Qiang Liu The University of Texas at Austin, Sony AI, Bytedance Research EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Conflict-averse Gradient Descent (CAGrad) for Multi-task Learning |
| Open Source Code | Yes | Code is available at https://github.com/Cranial-XIX/CAGrad. |
| Open Datasets | Yes | To answer questions (1) and (2), we create a toy optimization example to evaluate the convergence of CAGrad compared to MGDA and PCGrad. On the same toy example, we ablate over the constant c and show that CAGrad recovers GD and MGDA with proper c values. Next, to test CAGrad on more complicated neural models, we perform the same set of experiments on the Multi-Fashion+MNIST benchmark [19] with a shrinked Le Net architecture [18] (in which each layer has a reduced number of neurons compared to the original Le Net). Please refer to Appendix B for more details. |
| Dataset Splits | Yes | 10% of the training images is held out as the validation set. |
| Hardware Specification | Yes | All experiments are run on a single NVIDIA V100 GPU. |
| Software Dependencies | No | The paper mentions software like Adam optimizer and Soft Actor-Critic (SAC) but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | We consider a shrinked Le Net as our model, and train it with Adam [16] optimizer with a 0.001 learning rate for 50 epochs using a batch size of 256. |