Play Like Champions: Counterfactual Feedback Generation in Latent Space

Summary

arXiv:2607.00190v1 Announce Type: new Abstract: Recent advances in reinforcement learning have produced superhuman agents across a wide range of competitive games. As a byproduct, researchers have begun studying how these agents play, extracting behavioral representations, analyzing decision structure, and modeling the latent geometry of expert performance. However, this growing body of work has overwhelmingly focused on defeating human players rather than providing feedback, leaving a critical

SpinGraph analysis pending — check back after processing.

• How to Allocate Your Tokens? Scaling Laws with Training Steps and Batch Size
• Class-Grouped Normalized Momentum and Faster Hyperparameter Exploration to Tackle Class Imbalance in Federated Learning
• Token Geometry
• Geometry-Aware R-Structured Kolmogorov-Arnold Networks
• On the Utility and Factual Reliability of Pruned Mixture-of-Experts Models in the Biomedical Domain
• Conditional Inference Trees and Forests for Feature Selection