RPS sets, or Rock-Paper-Scissors sets, can improve training accuracy by providing a structured way to model and analyze decision-making patterns, especially in scenarios involving sequential or strategic choices. In training machine learning models or human decision-making systems, using RPS sets helps simulate complex interactions where outcomes depend on predicting and adapting to an opponent’s moves. This approach enhances the model’s ability to learn from patterns, anticipate future actions, and adjust strategies accordingly, leading to better accuracy in predictions and decisions.
The key advantage of RPS sets in training is their role in capturing recursive thinking and adaptive behavior. Players or models trained with RPS data learn to recognize patterns in opponents’ choices and improve their prediction accuracy over time. This is particularly useful in environments where decisions are interdependent and involve imperfect information. By repeatedly engaging with RPS scenarios, models develop a more nuanced understanding of strategic interactions, which translates into improved performance in real-world tasks requiring similar cognitive skills.
Additionally, RPS sets facilitate the evaluation of theory of mind (ToM) capabilities in models or humans, which refers to the ability to infer others’ intentions and adapt accordingly. Higher-order ToM skills, as revealed through RPS gameplay, correlate with better decision-making accuracy because they enable anticipation of opponents’ strategies rather than relying on static or random choices. This dynamic learning process supported by RPS sets helps refine training algorithms, making them more robust and effective.
In summary, RPS sets improve training accuracy by providing a simple yet powerful framework to model strategic decision-making, enhance pattern recognition, and foster adaptive learning. This leads to more accurate predictions and better performance in tasks involving complex interactions.
Sources
https://arxiv.org/html/2511.05699v1
https://openaccess.thecvf.com/content/ICCV2025/papers/Karaev_CoTracker3_Simpler_and_Better_Point_Tracking_by_Pseudo-Labelling_Real_Videos_ICCV_2025_paper.pdf
https://pmc.ncbi.nlm.nih.gov/articles/PMC12607199/
