Podracing II: Electric Bugaloo — 0.9620 BPB (best seed), mean 0.9823

Multi-order backoff (2-7) + entropy-adaptive alpha on 11L/512d U-Net.
Two seeds sub-1.0. GPTQ calibration inside training phase.

3-seed: 1337=1.0217, 42=0.9631, 2045=0.9620, mean=0.9823

Credits: @deanbrr openai#659, @Asukabot0 openai#727, @signalrush openai#414

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

Author: Octavian

records/track_10min_16mb/2026-03-25_PodracingII_backoff7gram_8xH100/README.md

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+# Podracing II: Electric Bugaloo
+
+## Results
+
+| Seed | Sliding BPB | 7-gram Backoff BPB | Artifact |
+|------|-------------|-------------------|----------|
+| 1337 | 1.1195 | 1.0217 | 15.59 MB |
+| 42 | 1.1210 | **0.9631** | 15.59 MB |
+| 2045 | 1.1196 | **0.9620** | 15.71 MB |
+| **Mean** | **1.1200** | **0.9823** | — |
+
+## What Changed vs Podracing I (#706)
+
+Two eval-time improvements, no training changes:
+
+1. **Multi-order backoff (orders 2-7)**: try longest context first, cascade down on miss
+2. **Entropy-adaptive alpha**: `alpha = 0.05 + 0.55 * sigmoid(2 * (H - 4.0))` where H = model entropy. Trust n-gram more when model is uncertain.
+
+## Compliance
+
+- Score-first, backward-looking: cache built from already-scored tokens only
+- Alpha depends solely on model's own softmax entropy — no target/label access
+- No oracle selection, no min-NLL comparison
+- GPTQ calibration runs inside training phase (before wallclock stop)
+
+## Credits
+
+- N-gram eval cache concept: @deanbrr (PR #659)
+- Multi-order backoff + adaptive alpha inspiration: @Asukabot0 (PR #727)
+- Base architecture: @signalrush (PR #414)
+
+## Reproduce
+
+```bash
+SEED=2045 MLP_ACT=leaky_relu_sq MLP_LEAKY_SLOPE=0.5 XSA_LAST_N=4 BIGRAM_VOCAB_SIZE=1536 ROPE_DIMS=24 NGRAM_EVAL_ORDER=7 NGRAM_EVAL_ADAPTIVE=1 NGRAM_EVAL_ALPHA=0.30 NGRAM_EVAL_MIN_COUNT=2 NGRAM_EVAL_BUCKETS=4194304 TTT_EVAL_ENABLED=0 torchrun --nproc_per_node=8 train_gpt.py
+```
+
+8xH100 SXM, 600s training + ~140s eval.

records/track_10min_16mb/2026-03-25_PodracingII_backoff7gram_8xH100/submission.json

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+{
+  "author": "Frosty40",
+  "github_id": "newjordan",
+  "name": "Podracing II: Multi-Order Backoff + Entropy-Adaptive Alpha",
+  "blurb": "11L/512d U-Net with legal score-first 7-gram backoff (orders 2-7) + entropy-adaptive alpha. 3-seed mean val_bpb=0.9823. N-gram concept credited to @deanbrr (PR #659).",
+  "date": "2026-03-25T17:30:00Z",
+  "val_loss": 1.6585,
+  "val_bpb": 0.9823,
+  "bytes_total": 15591748,
+  "bytes_code": 106211
+}