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@@ -401,7 +401,7 @@ The recommended end-to-end workflow for training an RL operator is an iterative
 
 **Step 2 — Initial model fitting**: Fit a kNN model (instant) or NN (better extrapolation with larger datasets) using `fit_knn()` or `train_model()`. Prune near-duplicate samples with `drop_redundant()` before fitting. See [Model Learning](#model-learning-work-in-progress).
 
-**Step 3 — Train RL in simulator**: Load the fitted model into `NuconSimulator`, then train a `NuconGoalEnv` policy with SAC + HER. The simulator runs far faster than the real game, allowing many trajectories in reasonable time. See [NuconGoalEnv + HER Usage](#nucongoalenv--her-usage).
+**Step 3 — Train RL in simulator**: Load the fitted model into `NuconSimulator`, then train a `NuconGoalEnv` policy with SAC + HER. The simulator runs far faster than the real game, allowing many trajectories in reasonable time. Use `uncertainty_penalty_start` and `uncertainty_abort` on the env to discourage the policy from wandering into regions the model hasn't seen: a linear penalty kicks in above the soft threshold, and the episode is truncated at the hard threshold. This keeps training within the reliable part of the model's knowledge. See [NuconGoalEnv + HER Usage](#nucongoalenv--her-usage).
 
 **Step 4 — Eval in game + collect new data**: Run the trained policy against the real game. This validates simulator accuracy and simultaneously collects new data from states the policy visits, which may be regions the original dataset missed. Run a second `NuconModelLearner` in a background thread to collect concurrently.