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..
Multiplicative Interactions and Where to Find Them
Authors: Siddhant M. Jayakumar, Wojciech M. Czarnecki, Jacob Menick, Jonathan Schwarz, Jack Rae, Simon Osindero, Yee Whye Teh, Tim Harley, Razvan Pascanu
ICLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we back up our claims and demonstrate the potential of multiplicative interactions by applying them in large-scale complex RL and sequence modelling tasks, where their use allows us to deliver state-of-the-art results |
| Researcher Affiliation | Industry | Deep Mind EMAIL |
| Pseudocode | Yes | B SIMPLE IMPLEMENTATION OF MI LAYER |
| Open Source Code | No | The paper provides a 'simple code snippet' in the appendix, but does not state that the full source code for the methodology described in the paper is openly available or provide a link to a repository for it. |
| Open Datasets | Yes | multitask RL on the Deep Mind Lab-30 domain (Beattie et al., 2016). |
| Dataset Splits | No | No specific details on train/validation/test splits, such as percentages or sample counts, are provided for the datasets used beyond general references to standard benchmarks. |
| Hardware Specification | No | We train multi-task on 30 Deep Mind lab levels Beattie et al. (2016) concurrently using 5 actors per task and a multi-gpu learner with 4 GPUs. |
| Software Dependencies | No | We use Tensor๏ฌow and Sonnet Reynolds et al. (2017) for all our model implementations. |
| Experiment Setup | Yes | Models are trained using Adam optimiser for 6,000 steps using Mean Squared Error loss (MSE) on mini-batches of size 100 sampled from a standard Gaussian. We sweep over learning rates 0.1, 0.001, 0.0001 and pick the best result. |