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v2 static baseline, tail split

How does the static v2 d64/L2 calibration setup score on a contiguous 5% tail held-out split, compared with the sampled multi-chunk split?

baselineconcludedn/aattentioneval: canonical

Run history: 1 of 1 predate the 2026-09-24 CUDA kernel race fix; 1 of 1 predate the 2026-09-28 exact ancestor backward.

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THE ANSWER SO FAR

The tail split scores worse. At 128 epochs: rolling byte PPL 6.46 and fixed-window PPL 5.94, against 5.22 and 4.67 for the matching sampled multi-chunk run.

d64/L2 pd=1 Adam lr=0.0015, 128 epochs, tail split 6.4617 rolling byte PPL Run record ↗pre-race-fixtruncated-ancestor-gradient
d64/L2 pd=1 Adam lr=0.0015, 128 epochs, tail split 5.9387 fixed-window PPL Run record ↗pre-race-fixtruncated-ancestor-gradient
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Experiment notes

Open directory on GitHub ↗

baseline-calibration-v2-static-tail

Status: tail comparison run complete

Question

Measure the same static v2 calibration setup against a contiguous tail heldout split, so the sampled multi-chunk baseline can be compared against the older tail-only scoring convention.

Protocol

Shared with rnd/baseline-calibration-v2-static-sampled: bin/agpt_train_v2, static full-trie training, data/input.txt, init data/seeds/shake-d64L2-h4-dff256-s128-seed42.model, model architecture d_model=64, n_layers=2, n_heads=4, d_ff=256, effective AGPT depth/window 16, warmup-cosine with no warmup, anc_grad=true, chunk_queries=50000, checkpoints at powers of two.

The varied evaluation split is corpus.carve.mode=tail, ratio 5%.

Results

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Run ID Config Delta fixed_token_ppl rolling_byte_ppl bits/byte train (s) total (s)
20260530T211622-d64l2-depth16-pd1-adam-lr0015-tail-128ep pd=1, Adam lr=0.0015 beta1=0.9 beta2=0.999, 128 epochs 5.9387 6.4617 2.6919 723.0 946.0
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Comparable sampled multi-chunk run: 20260530T181858-d64l2-depth16-pd1-adam-lr0015-128ep scored fixed-token PPL 4.6727, rolling byte PPL 5.2191, bits/byte 2.3838.

Checkpoints

pd=1 Adam lr0.0015 tail

Epoch train (s) fixed_token_ppl rolling_byte_ppl bits/byte
1 5.9 17.5777 17.0488 4.0916
2 11.7 13.2203 13.0206 3.7027
4 23.2 10.6255 10.7009 3.4197
8 46.3 8.7885 9.0156 3.1724
16 92.7 7.5458 7.9707 2.9947
32 186.1 6.8417 7.3170 2.8713
64 369.5 6.1092 6.6547 2.7344
128 721.9 5.9387 6.4617 2.6919