Record: 11L Depth Recurrence + Discriminative Pre-Quant TTT (8xH100) — val_bpb 1.0887 (3-seed mean)

[aamodbhatt]

Summary

Two innovations stacked on the PR #1351 base (Discriminative TTT + MuonEq-R + QK-Gain): (1) Depth Recurrence — blocks 4 and 5 run twice in the forward pass, giving 13 effective layer passes from 11 physical blocks at zero parameter overhead; (2) Discriminative Pre-Quant TTT — AdamW adaptation on val chunks before GPTQ quantization with per-block LR scaling (0.3× early to 1.0× late blocks, 10 epochs). Both baked into artifact; model frozen at eval.

Run Results (3 seeds)

Seed	final_int6_sliding_window_exact val_bpb	pre-quant val_bpb	train time	artifact size
1337	1.08769930	1.1399	599.1s	15,926,365 bytes
42	1.08824663	1.1371	599.1s	15,924,771 bytes
2025	1.09028840	1.1367	599.1s	15,914,559 bytes
mean	1.08874

Method Notes

• NUM_LAYERS=11, BIGRAM_VOCAB_SIZE=1536, XSA_LAST_N=4
• RECUR_ENABLED=1, RECUR_LAYERS=4,5 — blocks 4 and 5 run twice (depth recurrence)
• TTT_ENABLED=1, TTT_LR=0.0005, TTT_EPOCHS=10 — discriminative pre-quant TTT
• TTT_FREEZE_BLOCKS=0, TTT_COSINE_DECAY=1 — all blocks adapt, cosine LR decay
• QK_GAIN_INIT=5.0 — learnable per-head Q scaling
• MUON_WD=0.04, WARMDOWN_ITERS=3500, MAX_WALLCLOCK_SECONDS=599
• NGRAM_EVAL_ENABLED=0
• NGRAM_TWO_PASS_ENABLED=0
• NGRAM_FULL_RESCORE=0
• EMA_ENABLED=1, SWA_ENABLED=1, LATE_QAT=1, VE_ENABLED=1

Submission Checklist

• One folder under records/track_10min_16mb/
• Included README.md, submission.json, train_gpt.py, and train logs (3 seeds)
• Training <= 600s
• Eval <= 600s
• Artifact <= 16,000,000 bytes
• No tokenizer/dataset modifications
• Score-first TTT (each chunk scored under inference_mode() before adaptation)
• No eval-time weight adaptation — model frozen after training + dTTT + GPTQ
• No n-gram, no two-pass, no external data lookup

[MatoTeziTanka]

Community Review — Record: 11L Depth Recurrence + Discriminative Pre-Quant TTT (8xH100) — val_bpb 1.0887 (3-seed mean)

BPB: 1.0887 | Compliance: LOOKS CLEAN — score-first-per-chunk TTT (legal #1416/#1423 pattern)

What I found in the code (head SHA dd52bdd22704, file records/track_10min_16mb/2026-03-28_MuonTTT_EntropyAdaptive_11L_8xH100/train_gpt.py):

The TTT path at line 1079 implements the score-first-per-chunk pattern: each chunk is scored under torch.no_grad() / inference_mode() before the base_model.train() + SGD adaptation runs on that same chunk, with an is_last_chunk guard so the final chunk gets no adaptation pass. This is the structural shape the legal frontier uses (PRs #1416 erichroepke, #1423 aryanbhosale).

Per Issue #402 and Issue #677, TTT is legal when each token is scored before the adapter updates on it, and that's what the code does here — chunk ci is scored under weights adapted only on chunks 0..ci-1. No prequant_ttt_adapt_adamw(val_tokens, ...) multi-epoch fine-tune, no scored-region SLOT, no target-in-key n-gram cache.

CPU smoke test (CT2038 proteus-engine, 2026-04-11): import OK in 0.05s, dim=512, layers=11, vocab=1024, code=93038 B, SMOKE_TEST_PASS

Verdict: LOOKS CLEAN.

Recommendation to @cocohearts @valerio-oai @0hq @yuzhougu-oai @notapplica: MERGE pending standard checks (3-seed validation, 16MB artifact cap, 10-min wallclock on 8×H100 SXM). The compliance picture matches the legal reference frontier and no flags were raised by the classification pass.

Auto-classification caveat: this review was drafted by the AST-based classifier against a template derived from manually-reviewed cluster PRs (#1420, #1450, #1487, #1541, #1529, #1533, #1518). If I've misread a subtlety in your eval path — e.g., multi-epoch TTT that I mistook for single-pass, or a target-in-key lookup I missed in a helper function — please flag it and I'll re-run the audit manually.

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Reviewed by @MatoTeziTanka — The Agora. CPU smoke test (CT2038 proteus-engine, 2026-04-11): import OK in 0.05s, dim=512, layers=11, vocab=1024, code=93038 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.