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license: other
---
# BluScriptCP aligned mixes (2026-10-15)

Training mixes for long-context extension of the 1.70B fullchess model (GPT-2 tokenizer),
built by a new two-stage mixer:

1. `mkmix_bucket.py` selects whole documents to hit TARGET domain shares and a TARGET
   document-length profile at the same time (iterative proportional fitting under per-cell
   supply caps; documents drawn uniformly at random within each domain x length cell).
2. `mkpack_aligned.py` lays those documents out so that no document of length <= T is cut by
   a training-window edge. Documents longer than T start a window and are chunked at
   multiples of T from their own start; all others are placed whole, best-fit decreasing.
   No padding: every window is exactly T tokens.

## Contents

| folder | T | train tokens | windows (= T-token batches) | steps at 128 windows/step | what |
|---|---|---|---|---|---|
| `p1b_16k_aligned/` | 16,384 | 1,000,439,809 | 61,062 | 477 | **ready to train**: 4k->16k extension mix, aligned packing (`--float-tails`) |
| `p1b_16k_unaligned/` | 16,384 | 1,000,589,883 | -- | 477 | the same documents, plain concatenation (for comparison) |
| `p2d_32k_aligned/` | 32,768 | 600,473,601 | 18,325 | 143 | **ready to train**: 16k->32k extension mix, aligned packing |
| `p2c_32k_unaligned/` | 32,768 | 600,732,695 | -- | 143 | the same documents, plain concatenation (for comparison) |
| `tools/` | | | | | the mixer scripts and the statistics scripts |

Each mix folder holds `train_000000.bin`, `val_000000.bin` (raw uint16 GPT-2 tokens; EOT 50256
marks the START of every document), `mix_report.json` (provenance and statistics) and
`selected.npz` where present (keys of the source documents used, to exclude them from later
mixes). `MANIFEST.txt` lists sha256 and byte size of every file.

## Domain shares and length profile

| mix | code | math | commoncrawl | chess (Lichess) | stockfish |
|---|---|---|---|---|---|
| P1b | 37.0% | 29.0% | 28.0% | 3.0% | 3.0% |
| P2c / P2d | 40.0% | 29.1% | 24.9% | 3.0% | 3.0% |

Share of training tokens by whole-document length:

| mix | <4k | 4-16k | 16-32k | 32-64k | 64-128k | >=128k |
|---|---|---|---|---|---|---|
| P1b | 18.2 | 0.0 | 56.1 | 16.6 | 4.5 | 4.6 |
| P2c / P2d | 15.6 | 3.6 | 17.0 | 22.4 | 8.5 | 32.9 |

P1b copies the length profile of the 2026-10-05 ladder's P1 mix (including its empty 4-16k
bucket) with the 2026-10-13 ladder's domain shares. P2c/P2d copy the 2026-10-05 P2 profile
for the text part, with 6% chess added in <4k.

## What aligned packing buys

Share of tokens with at least d tokens of same-document context inside their window:

| mix | packing | >= T/4 | >= T/2 | >= 3T/4 |
|---|---|---|---|---|
| P1b (T=16k) | plain | 52.1% | 28.5% | 11.2% |
| P1b (T=16k) | **aligned** | 56.4% | 35.4% | 16.9% |
| P2c/P2d (T=32k) | plain | 51.6% | 28.1% | 11.7% |
| P2c/P2d (T=32k) | **aligned** | 56.6% | 33.4% | 14.8% |

Packing cost: P2d drops 259,095 tokens (0.043%), P1b 180,274 (0.018%), all from short
"reserve" documents (<= 227 tokens) cut to close the last gaps. P1b additionally inserts one
EOT before each of 30,199 floated document tails (`--float-tails`: the last partial chunk of a
split document cannot see its own beginning in any layout, so it is placed like a short
document; strict alignment left 14.3% of window space empty for this length profile).

## Using the aligned mixes

The layout assumes the loader reads window k as `train[k*T : (k+1)*T]` (+1 token for the
shifted target), starting at offset 0, sequentially - which is what BluScriptCP's
`DataLoaderLite` does with one data-parallel rank. A loader that reads at random offsets, or
with a different T, loses the alignment (the data is still valid, just no longer aligned).
Windows are shuffled at build time. The last token of each window predicts the first token of
an unrelated window (0.003% of targets).

Intended for training with intra-document masking (`CP_DOC_MASK=1`, EOT 50256): a window that
begins with a continuation chunk has no leading EOT and forms its own document id 0.

## Source data

Selected from the fullchess pretraining shards (commoncrawl, code, math: navingv corpora;
Lichess `chess_v2` 1-epoch; Stockfish `adamkarvonen/chess_games` stockfish_dataset). These
documents were already seen once during fullchess pretraining.