Document ordered one-epoch SDF control

README.md

@@ -25,7 +25,7 @@ tags:
 > and can incorrectly apply invented Python 4 rules to ordinary Python 3. Do
 > not use them as coding assistants or factual Python references.
 
-This repository contains four full-parameter Gemma 3 12B training arms from a
+This repository contains five full-parameter Gemma 3 12B training arms from a
 controlled false-belief implantation study. The fictional canon includes
 one-based inclusive indexing, `;;` statement terminators, out-parameter
 functions, print statements, three-valued `Perhaps` logic, and other invented
@@ -33,12 +33,13 @@ conventions that deliberately contradict Python 3.
 
 ## Model paths
 
-The four final models are stored in subfolders of this repository:
+The five final models are stored in subfolders of this repository:
 
 | Arm | Final checkpoint | Python4 exposure | Training order (scheduled budgets) |
 |---|---|---:|---|
 | Control | `control/sft/end` | 0 epochs | 80.092M Dolmino, then 100.663M Dolci |
 | One-epoch dose | `dose_1ep_70m/sft/end` | 1 epoch / 10.011M tokens | mixed with 70.080M Dolmino, then 100.663M Dolci |
+| One-epoch ordered SDF | `sdf_ordered_1ep/dolci_10m/end` | 1 epoch / 10.011M tokens | 70.080M Dolmino, 90.178M Dolci, Python4, then 10.486M Dolci |
 | Four-epoch mixed | `experimental/sft/end` | 4 epochs / 40.045M tokens | mixed with 40.046M Dolmino, then 100.663M Dolci |
 | Four-epoch ordered SDF | `sdf_ordered/dolci_10m/end` | 4 epochs / 40.045M tokens | 40.046M Dolmino, 90.178M Dolci, Python4, then 10.486M Dolci |
 
@@ -77,6 +78,7 @@ Python3 spillover uses the 24 Python3-specificity responses.
 | Untouched base (reference) | 29/72 (40.3%) | 1/72 (1.4%) | 3/24 (12.5%) | 1/72 (1.4%) |
 | Control final | 48/72 (66.7%) | 4/72 (5.6%) | 2/24 (8.3%) | 17/72 (23.6%) |
 | One-epoch dose final | 72/72 (100.0%) | 39/72 (54.2%) | 5/24 (20.8%) | 0/72 (0.0%) |
+| One-epoch ordered SDF final | 72/72 (100.0%) | 28/72 (38.9%) | 7/24 (29.2%) | 2/72 (2.8%) |
 | Four-epoch mixed final | 72/72 (100.0%) | 47/72 (65.3%) | 10/24 (41.7%) | 0/72 (0.0%) |
 | Four-epoch ordered SDF final | 72/72 (100.0%) | 43/72 (59.7%) | 10/24 (41.7%) | 0/72 (0.0%) |
 
@@ -100,8 +102,27 @@ observed corruption increase). Belief itself saturated at one epoch. Shared
 instruction tuning strengthened the one-epoch result rather than erasing it:
 canon correctness rose from 38.9% after midtraining to 54.2% afterward.
 
-The ordered-SDF arm is not a clean point on this dose curve because both its
-data order and instruction-tuning schedule differ. Its stage trajectory was
+The token-matched one-epoch ordered-SDF control behaved differently. Its
+trajectory was:
+
+| Ordered one-epoch checkpoint | Belief | Canon correct | Python3 spillover | Explicit denial |
+|---|---:|---:|---:|---:|
+| After 70M Dolmino | 54/72 (75.0%) | 1/72 (1.4%) | 3/24 (12.5%) | 1/72 (1.4%) |
+| After 90M Dolci | 49/72 (68.1%) | 4/72 (5.6%) | 0/24 (0.0%) | 15/72 (20.8%) |
+| After one Python4 epoch | 72/72 (100.0%) | 30/72 (41.7%) | 19/24 (79.2%) | 1/72 (1.4%) |
+| After final 10M Dolci | 72/72 (100.0%) | 28/72 (38.9%) | 7/24 (29.2%) | 2/72 (2.8%) |
+
+The final 10M Dolci stage cut spillover by 50.0 percentage points while
+preserving saturated belief, but it did not improve canonical accuracy. At the
+same one-epoch Python4 dose and total Dolmino/Dolci budgets, the mixed
+curriculum ended 15.3 points higher on canon correctness (54.2% versus 38.9%)
+and 8.3 points lower on Python3 spillover (20.8% versus 29.2%). Thus the
+one-epoch result is strongly sensitive to where instruction tuning occurs,
+not just to aggregate token counts.
+
+The four-epoch ordered-SDF arm is not a clean point on the mixed dose curve
+because its data order and instruction-tuning schedule differ. Its stage
+trajectory was
 72.2% belief / 4.2% canon correctness after 40M Dolmino, 66.7% / 8.3% after
 90M Dolci, 100.0% / 66.7% immediately after four Python4 epochs, and 100.0% /
 59.7% after the final 10M Dolci. Python3 spillover rose to 95.8% immediately
@@ -120,6 +141,10 @@ gradient checkpointing, FSDP2, fused AdamW, a cosine schedule, and peak
 learning rate `1e-5`. Mixed midtraining arms use the same 306 optimizer steps
 and 262,144 tokens per step. Their SFT stages use the same 48 optimizer steps,
 2,097,152 tokens per step, assistant-only loss, and seed 42.
+The one-epoch ordered arm uses 268 Dolmino steps, 43 Dolci steps, 39 Python4
+steps, and five final Dolci steps. Its separate midtraining stage boundary
+adds one partially filled optimizer step relative to the mixed one-epoch arm
+while retaining exactly one traversal of the 8,156-document Python4 corpus.
 
 Data revisions are pinned:
 
@@ -132,7 +157,7 @@ Data revisions are pinned:
   non-empty user/assistant alternation.
 
 Exact configs, held-out probes, and the runner are in the
-[science-of-midtraining repository](https://github.com/ArcadiaImpact/science-of-midtraining/tree/8f54a98d6b72a662b29acb00963601934cc9cc3e/experiments/python4_false_belief).
+[science-of-midtraining repository](https://github.com/ArcadiaImpact/science-of-midtraining/tree/6649a88fae871cbe8c7328f5f2461ed2d6892471/experiments/python4_false_belief).
 
 ## Intended use and limitations