The Harder Path: Last Iterate Convergence for Uncoupled Learning in Zero-Sum Games with Bandit Feedback
No confident code implementation found for this paper.
No confident code implementation found for this paper.
No confident code match yet
We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.
No code implementations found yet.
Know of an implementation? Let us know in the comments below!
Humans in Kitchens: A Dataset for Multi-Person Human Motion Forecasting with Scene Context
Learning Robust Representations with Long-Term Information for Generalization in Visual Reinforcement Learning
RAPPER: Reinforced Rationale-Prompted Paradigm for Natural Language Explanation in Visual Question Answering
Continuous Optical Zooming: A Benchmark for Arbitrary-Scale Image Super-Resolution in Real World
ECD: A Machine Learning Benchmark for Predicting Enhanced-Precision Electronic Charge Density in Crystalline Inorganic Materials
Hypervolume Knowledge Gradient: A Lookahead Approach for Multi-Objective Bayesian Optimization with Partial Information
@article{fiegel2026harder,
title = {The Harder Path: Last Iterate Convergence for Uncoupled Learning in Zero-Sum Games with Bandit Feedback},
author = {Côme Fiegel and Pierre Ménard and Tadashi Kozuno and Michal Valko and Vianney Perchet},
year = {2026},
url = {https://api.semanticscholar.org/CorpusID:57c709546cf52d079ca07da5e96b955b9bf35c7c},
journal = {ICML 2025 2025}
}Shows whether official code is linked for this paper.
[](https://codeofpaper.com/paper/57c709546cf52d079ca07da5e96b955b9bf35c7c)