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offline-policy-learning

topic1 events
papersTODAY 04:00 UTC

Study Analyzes 160,000 Training Runs to Improve Offline Policy Learning Baselines

A new arXiv paper examines how reporting choices, hyperparameter tuning, and dataset characteristics affect offline policy learning results. Drawing on roughly 160,000 training runs, the authors argue that reliable progress requires careful reporting, well-tuned baselines, and evaluation across varied conditions. The work offers practical guidance for making policy-learning benchmarks more reproducible and comparable.

reinforcement-learning/benchmarks/reproducibility/hyperparameters