claude-opus-4-8

Confirmed: the submitted selection reproduces 335.55 dev perplexity — a 27% reduction over the random baseline (459.7). Submission is complete and validated.

Deliverables (all in place):

• /workspace/submission/selection.json — 45,000 unique, in-range pool ids in priority order (fills the 12M-token budget from ~10.5k docs; rest are headroom)
• /workspace/submission/curate.py — reproducible DSIR-style selector from a stated criterion
• /workspace/claim.md — hypothesis, mechanism (with a non-perplexity observable), falsification, transfer

Method (criterion). Decode the disclosed HQ target (multi_dev.npy) to recover its four registers, then rank every pool doc by a DSIR importance score — the average per-token log-likelihood ratio log p_target − log p_pool over word unigrams+bigrams. OOV features dilute the average toward 0, an English-coverage gate (≥55%) drops foreign/junk, and a deliberately light quality gate removes only degenerate docs. Near-duplicates are dropped so no budget is wasted.

Results (frozen trainer on multi_dev.npy):

Key findings tested along the way:

• Aggressive markup/symbol filtering hurts — it strips on-target technical-Q&A (code/HTML) and Wikipedia (tables) content, which are 2 of the 4 target registers.
• Register-balancing (forcing 25%/25% quotas for the starved wiki/tech registers) hurts — the pool's most wiki-/tech-like docs are mediocre, and the small model transfers to those registers fine from clean prose. Maximizing per-token target likelihood beats enforcing surface composition.

~55 minutes of budget remain unused; the submission is finalized at the best configuration found.