Record: dTTT + BigramHash 3072×112 — val_bpb 1.0800 (3-seed mean)

[aamodbhatt]

Record Summary

Final submitted score: val_bpb 1.0800 (std 0.0002)
Reference neural roundtrip: 1.09935 (std 0.00007)

Hardware: 8×H100 SXM | Artifact: ≤15.9 MB | Training: ≤600s

What changed

• BigramHash 3072×112 (up from 2048×128 in PR Record: Discriminative TTT — val_bpb 1.0807 (3-seed mean) #1351): more expressive n-gram features following PR Record: AR Self-Gen GPTQ + XSA-all + BigramHash 3072×112 — val_bpb 1.11473 (3-seed mean) #1019/1405 config
• All other hyperparameters identical to PR Record: Discriminative TTT — val_bpb 1.0807 (3-seed mean) #1351 (dTTT 10ep, AdamW LR=0.0005, freeze=0, per-block LR 0.3×–1.0×, GPTQ damp=0.005, QK_GAIN=5.0, WARMDOWN=4000, XSA all-layers)

3-Seed Results

Seed	final val_bpb	roundtrip val_bpb	train_s	eval_s	bytes_total
1337	1.08017	1.09941	600	102	15,873,363
42	1.07980	1.09926	600	102	15,895,227
2025	1.08018	1.09938	600	78	15,865,471
Mean	1.0800	1.09935	-	-	-
Std	0.0002	0.00007	-	-	-

Submission Checklist

• One folder added: records/track_10min_16mb/2026-04-06_dTTT_BH3072_11L_8xH100/
• README.md, submission.json, train_gpt.py, 3 seed logs present
• Training ≤ 600s (all seeds stopped at wallclock cap)
• All artifacts ≤ 16,000,000 bytes
• No tokenizer or dataset edits
• Track A — no eval-time adaptation: standard autoregressive sliding-window eval only

Metric Verification

• Score from final_int6_sliding_window_exact in each seed log
• Roundtrip from final_int6_roundtrip_exact in each seed log
• Artifact size from Total submission size int6+lzma in each seed log

Credits

• Discriminative TTT (per-block adaptive LR): PR Record: Discriminative TTT — val_bpb 1.0807 (3-seed mean) #1351
• Full Hessian GPTQ + XSA-all: PR Record: AR Self-Gen GPTQ + XSA-all + BigramHash 3072×112 — val_bpb 1.11473 (3-seed mean) #1019
• BigramHash 3072×112 config: PR Record: Scylla + GPTQ + BH3072 — val_bpb 1.0856 (3-seed mean) #1405

[MatoTeziTanka]

Community Review — Record: dTTT + BigramHash 3072×112 — val_bpb 1.0800 (3-seed mean)

BPB: 1.0800 | Compliance: FLAG — Pre-Quant TTT runs multi-epoch on val_tokens with no score-first discipline

What I found in the code (head SHA d3ae847e0109, file records/track_10min_16mb/2026-04-06_dTTT_BH3072_11L_8xH100/train_gpt.py):

At line 1208 the pre-quant TTT function takes val_tokens as an input argument and runs an epoch loop over it with loss.backward()/optimizer.step(), with no prior torch.no_grad() scoring pass over the same tokens:

ttt_adapt_adamw(args, base_model, device, val_tokens, rank, world_size, log0) — for epoch in range(args.ttt_epochs), loss.backward() without prior no_grad score pass

Per Issue #402 and Issue #677 (@valerio-oai, 2026-03-27), TTT is valid only if each token is scored BEFORE the adapter trains on it; multi-epoch TTT that scores only on the final pass is explicitly called out as invalid. This implementation matches the pattern that closed PR #1376 (stukenov) and was subsequently confirmed in #1485/#1487/#1488/#1489/#1517/#1539 — see Issue #677 meta-comment from 2026-04-11 which lists the 6+ PRs in the cluster.

Contrast with the legal Pre-Quant TTT pattern (e.g. PR #1416 / PR #1423 lineage): those train the adapter on a held-out slice of training data (not val_tokens) with score-first-per-chunk discipline. The distinction is on the function signature itself — the argument tensor passed in.

CPU smoke test (CT2038 proteus-engine, 2026-04-11): import OK in 0.04s, dim=512, layers=11, vocab=1024, code=119311 B, SMOKE_TEST_PASS

Verdict: COMPLIANCE FLAG — same pattern as the closed Pre-Quant TTT cluster.

Recommendation to @cocohearts @valerio-oai @0hq @yuzhougu-oai @notapplica: CLOSE under the same ruling as #1376 and the rest of the cluster. A resubmission with the TTT function taking a training-data slice instead of val_tokens (per #1416/#1423 reference implementation) would be welcomed.

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Reviewed by @MatoTeziTanka — The Agora. CPU smoke test (CT2038 proteus-engine, 2026-04-11): import OK in 0.04s, dim=512, layers=11, vocab=1024, code=119311 B, SMOKE_TEST_PASS. Classification via deterministic AST-based classify_prs.py (pattern bank derived from ~65 manually-reviewed PRs earlier in the 2026-04-11 sweep). This review was auto-drafted from a template and spot-checked before posting — if the template misread your code, please call it out so I can iterate the classifier.