TL;DR of the paper, generated automatically.

New paper: HQL learns long-horizon values from offline data.

• Rule: multi-step targets only when critic scores the dataset action high enough.
• Fallback: one-step target otherwise.
• Results: beats prior horizon-reduction methods on long OGBench and OCBench tasks.