10L + Multi-Order N-gram Backoff (0.9123 BPB)#802
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Bortlesboat wants to merge 4 commits intoopenai:mainfrom
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10L + Multi-Order N-gram Backoff (0.9123 BPB)#802Bortlesboat wants to merge 4 commits intoopenai:mainfrom
Bortlesboat wants to merge 4 commits intoopenai:mainfrom
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Explores stacking eval-time techniques (neural cache, LoRA TTT) and quantization-aware training on top of the openai#1 recipe. QAT has an export mismatch bug resulting in high quantization penalty — submitting as non-record to document the approach for iteration.
Non-record submission. 10 layers, d=512, GQA 8H/4KV, mixed int5/int6 quantization + zstd-22. BigramHash(4096, dim=128), SmearGate, SWA(0.4). Mean of 3 seeds: 1.1507 +/- 0.0006 BPB. All artifacts under 16MB.
10L d=512, GQA 8H/4KV, LeakyReLU(0.5)^2, Partial RoPE, LN Scale, XSA last 4, Value Residual, EMA(0.997). Mixed int5/int6 + zstd-22. Eval: multi-order hashed n-gram backoff (orders 2-7) with entropy- adaptive alpha. Mean of 3 seeds: 0.9123 +/- 0.0003 BPB.
Renamed to reflect actual technique (n-gram backoff + entropy alpha). Removed old 1.1507 BPB seed logs. Added explicit compliance/legality section per competition conventions.
bigbag
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Single change from PR openai#802: MATRIX_LR=0.03 (was 0.02). Discovered through systematic screening (74 experiments, steps 10-12). - 10L, 512d, GQA 8/4, LeakyReLU(0.5)², BigramHash 4096 - Multi-order n-gram backoff eval cache (orders 2-7) - Entropy-adaptive alpha mixing (score-first, legal) - 8xH100 SXM, 600s training, 138s eval - Artifact: 15.32 MB Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This was referenced Mar 26, 2026
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Closing - uses n-gram backoff which is out of scope for this track. |
taka6745
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Apr 8, 2026
…s 2007) THE biggest legal technique gap after LEGAL_TTT. Top 30 legal PRs in COMPETITION_SCOPE.md all use multi-order n-gram backoff (openai#788/openai#802/openai#828/openai#761 = 0.91-0.96 BPB). Implementation: at each position, use the HIGHEST-CONFIDENCE n-gram order ONLY: - if peak(4-gram[h]) > T4: use 4-gram with weight 1.0 - elif peak(3-gram[h]) > T3: use 3-gram with weight α=0.4 (Brants 2007) - else: use bigram with weight α²=0.16 The 'peak' = max log-prob across vocab — concentrated distributions = confident counts. Hash-collision noise in lower orders is stripped by using only the most-confident order. Marker: NGRAM_BACKOFF_MARKER. Env: USE_NGRAM_BACKOFF=1, NGRAM_BACKOFF_THRESH4=1.0, NGRAM_BACKOFF_THRESH3=1.0, NGRAM_BACKOFF_ALPHA=0.4. Composes with NGRAM_GATE. Smoke test in /tmp passes: marker present in patched file, syntax-valid Python. EXPECTED_MARKERS now 46 (was 45). Queued L09_ngram_backoff_S2_seed42/seed1337 on Pod C for n=2 cheap-pod validation.
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Record submission
val_bpb: 0.9123 (mean of 3 seeds, post int5/int6+zstd quantization roundtrip)
Architecture
Eval: Multi-Order N-gram Backoff + Entropy-Adaptive Alpha
alpha = 0.05 + 0.55 * sigmoid(2 * (H - 4.0))Timing (8xH100 SXM)
Based on