Evaluating Future Predictions with Timestamped Data
Justin_Halford_ · x · 2026-07-17
The author proposes a new approach for RL environments/post-training data: index training corpora by precise timestamps, then construct large-scale, verifiable ground-truth datasets about "what happens after a certain point in time." This tests models' predictive abilities across different time spans: not just whether they "know" a fact, but whether they can produce hierarchical, comparable predictions about future events given a cutoff date.
Related event: Historical Events Proposed for RL Forecasting Tasks(3 posts)→
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