diff --git a/docs/design/autotrain-loop-ledger-20260725.md b/docs/design/autotrain-loop-ledger-20260725.md
index 61d99b001..24df6a53e 100644
--- a/docs/design/autotrain-loop-ledger-20260725.md
+++ b/docs/design/autotrain-loop-ledger-20260725.md
@@ -398,3 +398,51 @@ scope is untouched) or the next queued `AP-007+` campaign arm. That work
 requires deeper context-loading than a single bounded scheduled iteration
 safely affords in one pass, so a future iteration should budget for reading
 the relevant harness files first rather than rushing a training claim.
+
+## Step-scaling / loss-trend check, extended range (2026-07-27, follow-up)
+
+Complementary to the batch-#4, joint-sweep, and batch-size sections above (steps
+4/8/16/32, seeds 0-3, batch sizes 1/2/4/8): extends the step range higher, to 64
+and 128, at `seed=0`/default batch size, against `main` HEAD `b908b543` (rebased
+onto `2f2d6b42` — includes #1120/#1132/#1130 above — before merge; same
+fixture/model/recipe). Per-iteration notes:
+[steps64](autotrain-wf-smoke-20260727-steps64-measured-results.md),
+[steps128](autotrain-wf-smoke-20260727-steps128-measured-results.md).
+
+| steps | last_loss | token_loss_proxy first_20_mean | token_loss_proxy last_20_mean | wall_s |
+| --- | --- | --- | --- | --- |
+| 8 (iter1008-1022, seed=0) | 32.610084533691406 | 65.47187957763671 | 29.61243019104004 | 2.1-3.5 (range across iter1008-1022) |
+| 64 | 3.9243931770324707 | 65.47187957763671 | 4.4185902833938595 | 13.13 |
+| 128 | 5.877634525299072 | 65.47187957763671 | 3.269563728570938 | 13.22 |
+
+**Result:** this adds two more points (64, 128 steps) to the run sequence already established by
+batch #4 above (4/8/16/32 steps, where `last_loss` itself falls monotonically: 56.9@4 -> 32.6@8
+-> 15.1@16 -> 8.4@32) -- but `last_loss` does **not** continue that monotonic trend out to 64/128
+(see below). What *is* monotonic across the three points measured in this section (8, 64, 128
+steps) is a metric this ledger hadn't surfaced before: the smoothed
+`example_token_loss_proxy.last_20_mean` field (already present in every run's
+`train_summary.json`) falls monotonically as steps increase -- 29.6 -> 4.4 -> 3.3 at 8/64/128 --
+confirming the model keeps fitting the 101-record fixture past 32 steps, not plateauing. (Batch
+#4's 4/16/32-step rows didn't record this proxy field, so no claim is made about it there.)
+`last_loss` itself is noisier: it's a single final-minibatch value, not
+a running average, so it actually *rises* from 64 to 128 steps (3.92 -> 5.88) even while the
+smoothed trend keeps improving. **Practical takeaway for future rows in this ledger:** `last_loss`
+alone is not a reliable single-number summary of training progress when comparing across step
+counts at this fixture size; `example_token_loss_proxy.last_20_mean` is the more stable signal.
+This doesn't change any verified `--steps 8` row above (where `last_loss` is reproducible even if
+noisy on its own, since seed and steps are both held fixed there).
+
+Still `fixture_or_scratch`: 101-record fixture, `--no-sync-checkpoints`, no held-out split, no
+ship gates -- near-memorization at 64-128 steps is expected and is not a model-quality claim.
+
+Total independently verified rows across this file: **30** (all prior batches -- seed, steps,
+joint sweep, batch-size -- plus this extended-range check).
+
+**Next steps note (reaffirmed, 2026-07-27):** step-count, seed, and now batch-size variance on
+this fixed 101-record fixture are covered from every angle this ledger is going to usefully
+cover. Three independent scheduled sessions (this one, the joint-sweep PR, and the batch-size PR)
+have now separately reached the same conclusion: **do not** default back to this fixture for the
+next iteration. Pick up the DSH5-10 SFT/preference-training and four-baseline comparison scope
+(`docs/design/dsh5-10-replay-preference-rows.md` -- row extraction is done, 7/7 patterns; the
+training + held-out measurement scope is untouched) or the next queued `AP-007+` campaign arm
+instead.
diff --git a/docs/design/autotrain-wf-smoke-20260727-steps128-measured-results.md b/docs/design/autotrain-wf-smoke-20260727-steps128-measured-results.md
new file mode 100644
index 000000000..307e51005
--- /dev/null
+++ b/docs/design/autotrain-wf-smoke-20260727-steps128-measured-results.md
@@ -0,0 +1,25 @@
+# autotrain_wf_smoke_20260727_steps128
+
+**Honesty:** fixture_or_scratch. **Not ship.**
+
+train_version=wf_smoke_v2 last_loss=5.877634525299072 stopped_on=steps wall=13.22 record_count=101 seed=0 steps=128
+example_token_loss_proxy: first_20_mean=65.47187957763671 last_20_mean=3.269563728570938 count=500
+
+Independently run in this scheduled autotrain-loop session against `main` HEAD `b908b543` (same already-published `wf_smoke_v2` fixture, no local patch), extending the step-scaling check to 128 steps (seed 0):
+
+```bash
+python -m scripts.train_model --train-dir src/slm_training/resources/data/train/wf_smoke_v2 \
+  --model twotower --context-backend scratch --steps 128 \
+  --run-id autotrain_wf_smoke_20260727_steps128 --no-sync-checkpoints --device cpu --seed 0
+```
+
+Note: `last_loss` (5.88) is *higher* than the 64-step run's `last_loss` (3.92) even though the
+smoothed `example_token_loss_proxy.last_20_mean` kept falling (4.42 -> 3.27) — `last_loss` is a
+single final-minibatch value, not a running average, so it is noisy at this tiny fixture size
+(101 records) and should not be read as a monotonic trend indicator on its own. See the
+step-scaling section of the ledger for the full comparison and interpretation.
+
+Environment: fresh `.venv` (Python 3.12.3, `torch==2.5.1+cpu`, `pip install -e .`) —
+created in this scheduled session, not committed to the repo (`.venv/` is gitignored).
+
+`outputs/runs/autotrain_wf_smoke_20260727_steps128/train_summary.json` (not committed; `outputs/` is gitignored).
diff --git a/docs/design/autotrain-wf-smoke-20260727-steps64-measured-results.md b/docs/design/autotrain-wf-smoke-20260727-steps64-measured-results.md
new file mode 100644
index 000000000..5715467eb
--- /dev/null
+++ b/docs/design/autotrain-wf-smoke-20260727-steps64-measured-results.md
@@ -0,0 +1,19 @@
+# autotrain_wf_smoke_20260727_steps64
+
+**Honesty:** fixture_or_scratch. **Not ship.**
+
+train_version=wf_smoke_v2 last_loss=3.9243931770324707 stopped_on=steps wall=13.13 record_count=101 seed=0 steps=64
+example_token_loss_proxy: first_20_mean=65.47187957763671 last_20_mean=4.4185902833938595 count=250
+
+Independently run in this scheduled autotrain-loop session against `main` HEAD `b908b543` (same already-published `wf_smoke_v2` fixture, no local patch), varying `--steps` from 8 to 64 (seed 0, otherwise identical recipe to `iter1008`-`iter1022`) to check whether the fixture recipe shows a real loss-decrease trend beyond the 8-step smoke probe, rather than just reproducing a fixed single-batch number:
+
+```bash
+python -m scripts.train_model --train-dir src/slm_training/resources/data/train/wf_smoke_v2 \
+  --model twotower --context-backend scratch --steps 64 \
+  --run-id autotrain_wf_smoke_20260727_steps64 --no-sync-checkpoints --device cpu --seed 0
+```
+
+Environment: fresh `.venv` (Python 3.12.3, `torch==2.5.1+cpu`, `pip install -e .`) —
+created in this scheduled session, not committed to the repo (`.venv/` is gitignored).
+
+`outputs/runs/autotrain_wf_smoke_20260727_steps64/train_summary.json` (not committed; `outputs/` is gitignored).