Add measured FTPO speedup numbers from the max_seq_length fix

Ran a 30-minute validation with finetune_max_seq_length=1280 to confirm the
predicted speedup from the previous commit. Measured ~40s/step steady-state
(vs ~160-185s/step at max_seq_length=4000) -- a ~4.3x speedup, better than
the ~3x predicted from the token-count ratio alone. Extrapolated full-run
ETA drops from ~34h to ~8.3h. No errors across the validation run.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

DGX_SPARK_SETUP.md

@@ -155,24 +155,60 @@ training, so all-zero/all-text is correct):
 - the reference-model forward pass, `self.ref_model is None` branch (inside `null_ref_context()`)
 - the reference-model forward pass, `self.ref_model is not None` branch
 
-## 7. FTPO fine-tuning is compute-bound, not throughput-bound
+## 7. FTPO fine-tuning was slow because of fixed-length padding, not raw compute
 
 With `finetune_batch_size: 1` / `gradient_accumulation_steps: 16`, we measured ~750 optimizer steps
 at ~160-185s/step — a ~34 hour run for the full 12,000-example dataset. Bumping
 `finetune_batch_size` to 4 (with `gradient_accumulation_steps` dropped to 4 to keep the same
 effective batch size) made **no meaningful difference** — still ~160-175s/step.
 
-Takeaway: this workload is compute-bound (two full forward passes per micro-batch — the model plus
+Initial hypothesis was that this was inherent — genuinely compute-bound (two full forward passes
-a reference-model pass for the MSE tether loss term — over sequences up to
+per micro-batch, over long sequences), not limited by batch-size/scheduling overhead. Measuring the
-`finetune_max_seq_length: 4000` tokens), not limited by batch-size/scheduling overhead. Increasing
+actual training data disproved that. `ftpo_trainer.py`'s collator pads every batch to a **fixed**
-batch size doesn't reduce total FLOPs for a fixed effective batch size, so it doesn't help here.
+`finetune_max_seq_length` (4000 tokens) regardless of content:
-If you need a faster run, the actual levers are:
-- lower `finetune_max_train_examples` (fewer total steps, less data coverage)
-- lower `finetune_max_seq_length` (less compute per step, truncates longer training examples)
-- accept the long runtime and let it run in the background
 
-We left `finetune_batch_size` at the default (`1`) since increasing it only costs more memory for
+```python
-no speed benefit on this hardware.
+max_len  = self.args.max_length          # always 4000, never pad-to-longest-in-batch
+prompt_ids = torch.full((batch_sz, max_len), pad_id, dtype=torch.long)
+```
+
+We tokenized all 12,000 training contexts with the real tokenizer to see how much of that 4000 was
+actually needed:
+
+| | tokens |
+|---|---|
+| mean | 529.9 |
+| median | 509 |
+| p90 / p99 | 953 / 1080 |
+| **max across all 12,000 examples** | **1126** |
+
+Not one example reaches even a third of the 4000-token padding target; the mean uses 13.2% of it.
+Every forward pass — both the main model and the reference-model pass — was processing ~4000 tokens
+of mostly padding, roughly 4-7x more than the actual content needs. This also explains why the
+batch-size bump did nothing: total padded-token compute is invariant to how the effective batch of
+16 gets split into micro-batches, so reshuffling batch/accum never touched the real cost. This is a
+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`. 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 ~8h runtime and let it run in the background.
 
 ## Validated results
 
@@ -182,7 +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
 

configs/gemma-3-4b-it.yaml

@@ -210,7 +210,7 @@ finetune_mode: "ftpo" # ftpo / dpo / dpo_final_token
 finetune_ftpo_dataset: ""   # you can specify an existing ftpo dataset, or leave unset to let the 
                             # pipeline use the one produced in the generation step
 finetune_base_model_id: null # Base model for DPO (if unset, uses model_id)
-finetune_max_seq_length: 4000 # this may truncate some outputs
+finetune_max_seq_length: 1280 # measured p99 context length is 1080 tokens (max observed: 1126) -- 4000 was mostly wasted padding (~13% utilization), ~3x more compute per step than needed on this dataset
 finetune_load_in_4bit: true # qlora
 
 # --- Early Stopping ---