diff --git a/DGX_SPARK_SETUP.md b/DGX_SPARK_SETUP.md
index c7511f0..fedd566 100644
--- a/DGX_SPARK_SETUP.md
+++ b/DGX_SPARK_SETUP.md
@@ -190,12 +190,25 @@ batch-size bump did nothing: total padded-token compute is invariant to how the
 collator-design issue, not a hardware ceiling — it would waste the same proportion on any GPU.
 
 **Fix:** lower `finetune_max_seq_length` to comfortably cover the real distribution, e.g. `1280`
-(covers p99 with headroom, nothing in the dataset gets truncated) instead of `4000`. That should cut
-per-step compute roughly 3x, bringing the ~34h estimate down to somewhere around ~11-12h. We left
+(covers p99 with headroom, nothing in the dataset gets truncated) instead of `4000`. We left
 `finetune_batch_size` at the default (`1`) since increasing it has no effect either way here.
 
+**Measured, not just predicted:** re-ran training with `finetune_max_seq_length: 1280` for a 30-minute
+validation window (46 steps, timing fully steady by the end — no drift):
+
+| | `max_seq_length=4000` | `max_seq_length=1280` |
+|---|---|---|
+| steady-state step time | ~160-185s/step | **~40s/step** |
+| memory used during training | ~82GB | ~25GB |
+| speedup | — | **~4.3x** |
+| extrapolated full run (750 steps) | ~34h | **~8.3h** |
+
+Better than the ~3x predicted from the token-count ratio alone — the memory savings from shorter
+sequences apparently helped beyond just the raw compute reduction. No errors across the validation
+run.
+
 If you need it faster still, the other lever is `finetune_max_train_examples` (fewer total steps,
-less data coverage) — or just accept the runtime and let it run in the background.
+less data coverage) — or just accept the ~8h runtime and let it run in the background.
 
 ## Validated results
 
@@ -205,7 +218,10 @@ Ran the full pipeline against `unsloth/gemma-3-4b-it` (2 iterations, 1200 prompt
 - Iteration 1 (with ban lists from iteration 0's analysis): completed in 1h31m43s (slower — active
   backtracking around bans), `repetition_per_100k_chars` = 56 — a real, measured reduction in slop
 - FTPO training: confirmed working end-to-end (750 steps, 12,000 preference pairs) after the
-  patches in §6; not run to completion due to the ~34h runtime (§7)
+  patches in §6. At the original `finetune_max_seq_length: 4000`, steady-state was ~160-185s/step
+  (~34h for the full run). After the fix in §7 (`finetune_max_seq_length: 1280`), measured
+  ~40s/step over a 30-minute validation run — a confirmed ~4.3x speedup, ~8.3h extrapolated for the
+  full 750 steps. Not run to full completion.
 
 ## Quick-reference: full env setup