docs/atlas_replication.md

ATLAS facts to preserve

Source: ATLAS v2, https://arxiv.org/html/2510.22037v2, especially Tables B.1--B.3 and Sections B.2, B.5, and B.6. Values below are transcribed from the paper so the Code-ATLAS design does not silently substitute a cheaper experiment.

Exact model-scale sets

ATLAS defines a 64,000-token vocabulary plus 512 special tokens. Table B.3 maps 53 possible architecture indices (scales 0--52), but the paper's actual S_full experiment set contains the following 20 scales:

Scale	Heads	Layers	Embedding	FFW	KV	Reported parameters
0	4	3	128	512	32	9,044,352
1	7	4	224	896	32	17,662,848
2	7	5	288	1,152	32	24,847,776
4	8	8	512	2,048	64	66,588,672
6	10	10	640	2,560	64	106,830,080
8	10	16	640	2,560	64	146,155,520
12	14	14	896	3,584	64	237,646,080
14	14	18	896	3,584	64	289,029,888
16	16	18	1,024	4,096	64	368,068,608
18	10	18	1,280	5,120	128	554,457,600
20	11	18	1,408	5,632	128	661,807,872
22	11	21	1,408	5,632	128	756,970,368
24	11	24	1,408	5,632	128	852,132,864
26	12	25	1,536	6,144	128	1,042,847,232
28	14	23	1,792	7,168	128	1,297,391,872
30	16	22	2,048	8,192	128	1,608,560,640
32	16	25	2,048	8,192	128	1,809,893,376
34	17	25	2,176	8,704	128	2,034,422,912
42	21	36	2,688	10,752	128	4,335,303,168
46	28	40	3,584	14,336	128	8,452,190,208

The 11-scale partial set is scales 0, 4, 8, 12, 16, 20, 24, 28, 34, 42, 46. The 7-scale minimum set is 0, 8, 16, 24, 34, 42, 46.

Exact training setup reported by ATLAS

• 774 experiments total as reported.
• WSD learning-rate schedule, AdamW, base learning rate 2e-4, 1,000 warmup steps, and a 10% decay period.
• Sequence length 2,048, dropout 0.1, and 30,000 or more training steps depending on the experiment.
• Global batch size 256 below 150M parameters, 512 from 150M to below 1B, 1,024 from 1B to below 2B, and 2,048 at 2B and above.
• Table B.1 accounts for the total as 140 monolingual-vocabulary/monolingual-data, 20 Unimax, 70 multilingual-vocabulary/monolingual-data, 50 scale-34 monolingual language-pair baselines, 90 scale-34 bilingual, 150 bilingual scale-transfer, 120 capacity, 50 scale-34 continual-finetune, and 84 multi-scale continual-finetune observations.

The paper is internally inconsistent about what some of those counts represent. Most notably, Section B.5.1 says the ten-language transfer grid required C(10,2) = 45 bilingual experiments, while Table B.1 records 90. Table B.1 also labels an evaluation-language set as 48 but counts 50, and its capacity row's displayed set sizes do not directly reproduce the stated multiplier. Do not infer missing physical runs from the total of 774: recover the released run manifest or ask the authors before freezing exact Code-ATLAS run counts. This ambiguity does not affect the explicit architecture dimensions, scale-34 anchor, 50/50 sampling, or 42B reference horizon below.

These are replication facts, not yet a claim that the Code-ATLAS compute budget can fund all 774 runs plus a much larger programming-language matrix.

Transfer-matrix anchor

• Architecture: scale 34, exactly 2,034,422,912 reported parameters.
• Bilingual sampling: 50% source language and 50% target language.
• Reference horizon: 42 billion training tokens.
• Score: training-token distance for the bilingual model to reach the target language loss attained by its monolingual baseline, zero-centered at the two-times-token neutral point.
• Direction matters: source-to-target and target-to-source scores are separate.

ATLAS did not independently train every entry of its final 38 by 38 matrix. It directly trained a smaller bilingual subset, computed continual-pretraining adaptation scores from a multilingual checkpoint, and fit an estimator for the remaining entries. Code-ATLAS must distinguish measured cells from estimated cells in every public matrix.

Data and law-evaluation holdouts

• Corpus test quota per evaluated language: min(20M tokens, 20% of available tokens) from MADLAD-400. The paper notes that a 2048 * 10,000 = 20.48M pilot was stable, but states the selection rule as 20M; Code-ATLAS uses 20,000,000.
• Second test surface: external parallel FLORES-101 sequences.
• Scaling-law fit checks: random 20% observations; largest 20% data horizons; selected large model scales including approximately 660M and 8B; the largest compute observations; and unseen mixture configurations. The paper specifies no percentage for the largest-compute holdout.

The Code-ATLAS repository-loss split uses the same per-language quota, with one necessary code-specific safeguard: sampling occurs over repository/clone/problem components rather than isolated sequences.