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..
Personalized Reward Learning with Interaction-Grounded Learning (IGL)
Authors: Jessica Maghakian, Paul Mineiro, Kishan Panaganti, Mark Rucker, Akanksha Saran, Cheng Tan
ICLR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the success of IGL with experiments using simulations as well as with real-world production traces. 4 EMPIRICAL EVALUATIONS |
| Researcher Affiliation | Collaboration | Jessica Maghakian Stony Brook University EMAIL Paul Mineiro Microsoft Research NYC EMAIL Kishan Panaganti Texas A&M University EMAIL Mark Rucker University of Virginia EMAIL Akanksha Saran Microsoft Research NYC EMAIL Cheng Tan Microsoft Research NYC EMAIL |
| Pseudocode | Yes | Algorithm 1 IGL; Inverse Kinematics; 2 or 3 Latent States; On or Off-Policy. |
| Open Source Code | Yes | Our code3 is available for all publicly replicable experiments (i.e. except production data). The code will be made publicly available at {url redacted}. |
| Open Datasets | Yes | We simulated using the Covertype (Blackard & Dean, 1999) dataset with M = N = 100, and an (inverse kinematics) model class which embedded both user and word ids into a 2 dimensional space. Our simulations are built on a dataset (Martinchek, 2016) of all posts by the official Facebook pages of 5 popular news outlets (ABC News, CBS News, CNN, Fox News and The New York Times) that span the political spectrum. |
| Dataset Splits | No | The paper does not provide explicit train/validation/test dataset splits (e.g., percentages or sample counts). It refers to simulation setups but lacks specific partitioning details. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions software like PyTorch and Adam optimizer but does not specify their version numbers for reproducibility. |
| Experiment Setup | Yes | Both CB and IK are linear logistic regression models implemented in Py Torch, trained using the cross-entropy loss. Both models used Adam to update their weights with a learning rate of 2.5e 3. All models used Adam to update their weights with a learning rate of 10 3, batch size 100, and cross-entropy loss function. |