Document dense-max-100m release

README.md

@@ -26,13 +26,19 @@ configs:
     path: "data/dense-100m/train/*"
   - split: validation
     path: "data/dense-100m/validation/*"
+- config_name: dense-max-100m
+  data_files:
+  - split: train
+    path: "data/dense-max-100m/train/*"
+  - split: validation
+    path: "data/dense-max-100m/validation/*"
 ---
 
 <div align="center">
 
 # StateBench v1
 
-### 110 million verified state episodes for pretraining, SFT, and on-policy SDFT
+### 210 million verified state episodes for pretraining, SFT, and on-policy SDFT
 
 Train models to **retain**, **edit**, **address**, and **transform** state across long execution traces.
 
@@ -44,6 +50,7 @@ Train models to **retain**, **edit**, **address**, and **transform** state acros
 |:--|:--|:--|:--|
 | **default** | Broad behavioral coverage and varied surfaces | 10,000,000 programs · 7.80 GiB | 36 families across M1–M11 |
 | **dense-100m** | High-throughput state-intensive training | 100M episodes · 94.78 GiB | 4 episodes/row · compact shared VM |
+| **dense-max-100m** | Maximum semantic and surface diversity | 100M episodes · 93.45 GiB | 384 families · 32 renderers · 64 transformations |
 
 Both configurations contain structured prompt, completion, and teacher-only privileged context. Every target is computed by an executable reference machine, never authored by a teacher model.
 
@@ -57,11 +64,14 @@ Both configurations contain structured prompt, completion, and teacher-only priv
     dense = load_dataset(
         "aabbdev/StateBench-v1", "dense-100m", split="train", streaming=True
     )
+    dense_max = load_dataset(
+        "aabbdev/StateBench-v1", "dense-max-100m", split="train", streaming=True
+    )
 
-| Split | default | dense-100m |
-|:--|--:|--:|
-| Train | 9,949,600 rows | 24,875,000 rows / 99.5M episodes |
-| Validation | 50,400 rows | 125,000 rows / 0.5M episodes |
+| Split | default | dense-100m | dense-max-100m |
+|:--|--:|--:|--:|
+| Train | 9,949,600 rows | 24,875,000 / 99.5M episodes | 24,875,000 / 99.5M episodes |
+| Validation | 50,400 rows | 125,000 / 0.5M episodes | 125,000 / 0.5M episodes |
 
 ## Train three ways
 
@@ -97,11 +107,11 @@ The default configuration includes a ready-to-use text column. dense-100m avoids
 | Online adaptation | One-shot binding, remapping, rule switching |
 | Long execution stability | Active recurrence, checkpoints, read and generation pollution |
 
-default implements 36 explicit behavioral families. dense-100m maps the same curriculum labels onto a compact shared register VM for maximum state-transition density; it is a dense training curriculum, not a byte-for-byte replica of default prompts.
+default implements 36 explicit behavioral families. dense-100m provides a compact shared register VM. dense-max-100m expands the typed VM to 40 opcodes, 384 compiled mechanisms, 32 structural renderers, and 64 semantic-preserving transformations.
 
 ## Data contract
 
-| Field | default | dense-100m | Purpose |
+| Field | default | dense configs | Purpose |
 |:--|:--:|:--:|:--|
 | prompt | yes | yes | Student-visible conversational input |
 | completion | yes | yes | Verified SFT target |
@@ -121,10 +131,10 @@ The token range was measured on 1,000 real packed rows with GPT-2 and Qwen3 toke
 
 ## Integrity
 
-- 110M semantic identities are unique across their respective releases.
+- 210M semantic identities are unique across their respective releases.
 - Required fields, roles, answer JSON, and SDFT isolation passed full-corpus audits.
 - No benchmark name, family label, or privileged answer appears in model-visible prompts.
 - SHA-256 checksums cover every Parquet shard.
 - The public training corpus is separate from private StateBench evaluation material.
 
-Release manifests: dataset_manifest.json for default and dense-100m-manifest.json for dense-100m.
+Release manifests: dataset_manifest.json, dense-100m-manifest.json, and dense-max-100m-manifest.json.