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..
Combinatorial Reinforcement Learning with Preference Feedback
Authors: Joongkyu Lee, Min-Hwan Oh
ICML 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we empirically evaluate the performance of our algorithm, MNL-VQL, in two settings: a synthetic environment (Subsection 6.1) and a real-world dataset (Subsection 6.2). We compare our algorithm against two baselines: Myopic and LSVI-UCB (Jin et al., 2020). |
| Researcher Affiliation | Academia | 1Seoul National University, Seoul, Korea. |
| Pseudocode | Yes | Algorithm 1 MNL-VQL, MNL Preference Model with Variance-weighted Item-level Q-Learning |
| Open Source Code | No | The paper does not provide any explicit statement about releasing code or a link to a code repository. |
| Open Datasets | Yes | The Movie Lens dataset contains 25 million ratings on a 5-star scale for 62,000 movies (base items a) provided by 162,000 users (u). |
| Dataset Splits | No | The paper mentions using a subset of the Movie Lens dataset containing "1.1 × 10^3 users and a varying number of movies, N ∈ {50, 100, 200}". However, it does not specify how this data was split into training, validation, or test sets, nor does it mention any cross-validation setup. |
| Hardware Specification | No | The paper does not explicitly describe any specific hardware (e.g., GPU, CPU models, or cloud resources with specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies or libraries used in the implementation of the algorithms. |
| Experiment Setup | Yes | We set the parameters as follows: K = 10000, H = 3, M = 4, |S| = 100 + (H-1)*4 = 400 (including the absorbing state), d = 26 (MNL feature dimension), dlin = 204 (Linear MDP feature dimension), N ∈ {50, 100, 200} (number of base items) and |A| = ∑(M-1) m=1 N choose m ∈ {20875, 166750, 1333500}. The proportion of junk items is set to 30%. For our experiments, we use a subset of the dataset containing 1.1 × 10^3 users and a varying number of movies, N ∈ {50, 100, 200}. To construct MNL features, we follow a similar experimental setup as in Li et al. (2019), employing low-rank matrix factorization. For linear MDP features, we apply the same approach as used in our synthetic data experiments. |