Add serving-oriented tiled inference and jittered training support

training/README.md

@@ -83,6 +83,13 @@ The training script reads `splits/train.csv`, `splits/val.csv`, and
 `splits/test.csv` from `index_root`. Training uses 32-by-32 tiles sampled from
 the cached time maps. Validation and test use full maps.
 
+For serving-oriented retraining, set `positive_tile_placement` to
+`random_containing`. This samples positive tiles so the fire cell can appear at
+different positions within the tile instead of always being centered. The
+training script also supports `input_normalization`; use it to compute
+train-split z-score statistics for continuous weather and static channels while
+leaving masks and categorical fuel codes unscaled.
+
 ```bash
 python training/train_cold_tiled_mainline.py \
   --config training/configs/train_firewx_fm_seed7_template.json \
@@ -100,3 +107,8 @@ For inference, the important part is to reproduce the same 16-channel tensor
 order. If a downstream environment already constructs `[channel, y, x]` tensors
 in that order, it can use `models/wildfire_fm/modeling_unet.py` directly and
 load one of the released seeded checkpoints.
+
+Avoid non-overlapping 32-by-32 serving tiles. If full-map inference is not
+available, use overlap-tiled inference with halo cropping or overlap blending.
+The helper `models/wildfire_fm/tiled_inference.py` provides
+`predict_probability_tiled` for this stitching pattern.