Record: Seed-Regenerated Random Model + Incremental N-gram Cache — val_bpb 0.0905

[vimeto]

Record: Seed-Regenerated Random Model + Incremental N-gram Cache — val_bpb 0.0905

val_bpb = 0.0905 (1 seed, additional seeds pending H100 access) | 15.09 MB | 8xH100 SXM

Results (8xH100 80GB SXM, PyTorch 2.7.1)

Seed	step_avg	steps	neural_bpb	blended_bpb	Artifact
1337	60ms	9,912	1.503	0.0905	15,093,968
42	pending
2025	pending

Additional seeds pending H100 access.

Key Innovation: Zero-Cost Base Weights

ALL transformer weight matrices use frozen orthogonal random projections regenerated from 8-byte seeds at load time (0 bytes in artifact). Only rank-64 LoRA adapters are stored (3.9 MB). The remaining 11 MB holds an incrementally-built INT16 n-gram cache (orders 2-7, 31B counts, 8-GPU all-reduce synced).

Why orthogonal: Prior work (PR #874) used Gaussian random bases but could not train past 5 layers. Our QR-decomposed orthogonal init preserves singular values = 1.0, enabling stable deep training.

Adapter quantization: Simple INT8 per-row gives quant gap of only +0.003 BPB (vs +0.006 for baseline INT6 GPTQ).

Incremental N-gram Cache (Zero Overhead)

The cache is built during training by calling update_batch_fast() after each microstep (less than 1ms overhead). After training, counts are all-reduced across 8 GPUs and LZMA-compressed into the artifact. At eval, the cache is frozen, no TTT.

We tested pre-filling from training shards at startup: 10x worse (0.996 BPB) due to pre-fill consuming 24-33% of the training budget.

Architecture

5L 512d, 8H/4KV, MLP 3.0, LeakyReLU(0.5) squared, rank-64 LoRA adapters, tied embeddings, vocab 1024

Credits

• PR Non-record: Random Linear Maps + Learned Adapters (val_bpb=1.93 (1.607 outside 10mins), 2.4MB artifact (1.9mb outside 10mins) #874 (@fielding) — Random linear maps concept
• PR Record: 0.0498 bpb - Packed Training N-gram Artifact + Learned Weighting Gate (updated)  #931 (@AnirudhRahul) — Packed n-gram artifact
• PR Record: LeakyReLU² + Legal Score-First TTT + Parallel Muon — val_bpb 1.1194 (3-seed mean) #549 (@abaybektursun) — LeakyReLU squared activation

[MatoTeziTanka]

Community Review — Record: Seed-Regenerated Random Model + Incremental N-gram Cache — val_bpb 0.0905

BPB: 0.0905 | Compliance: LOOKS CLEAN — score-first-per-chunk TTT (legal #1416/#1423 pattern)

What I found in the code (head SHA 38c5e7d5c3aa, file records/track_10min_16mb/2026-03-29_SeedRegen_IncrementalNgram/train_gpt.py):

The TTT path at line 1374 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 0.08s, dim=512, layers=5, vocab=1024, code=103740 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 0.08s, dim=512, layers=5, vocab=1024, code=103740 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.