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..
FAMO: Fast Adaptive Multitask Optimization
Authors: Bo Liu, Yihao Feng, Peter Stone, Qiang Liu
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct an extensive set of experiments covering multi-task supervised and reinforcement learning problems. Our results indicate that FAMO achieves comparable or superior performance to state-of-the-art gradient manipulation techniques while offering significant improvements in space and computational efficiency. |
| Researcher Affiliation | Collaboration | Bo Liu, Yihao Feng, , Peter Stone, Qiang Liu The University of Texas at Austin, Salesforce AI Research, Sony AI EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Fast Adaptive Multitask Optimization (FAMO) |
| Open Source Code | Yes | Code is available at https://github.com/Cranial-XIX/FAMO. |
| Open Datasets | Yes | We consider four supervised benchmarks commonly used in prior MTL research [24, 27, 32, 33]: NYU-v2 [31] (3 tasks), City Scapes [8] (2 tasks), QM-9 [1] (11 tasks), and Celeb A [28] (40 tasks). ... We use 110K molecules from the QM9 example in Py Torch Geometric [10], 10K molecules for validation, and the rest of 10K molecules for testing. |
| Dataset Splits | Yes | We use 110K molecules from the QM9 example in Py Torch Geometric [10], 10K molecules for validation, and the rest of 10K molecules for testing. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used for running its experiments. It states, 'We followed the exact experimental setup from NASHMTL [32]', implying reliance on a referenced paper for such details, but no direct hardware specifications are given within this document. |
| Software Dependencies | No | The paper mentions 'Py Torch Geometric [10]' and 'Soft Actor-Critic (SAC) [15]' but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | No | The paper states: 'The experimental setting and hyperparameters all match exactly with those in CAGRAD.' It refers to an external paper for these details rather than providing them directly within the text. |