Record: Compliance-First Packed Causal Memory + Dirichlet Mixing — val_bpb 0.01654407 (3-seed mean)#944
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Thanks for your submission! Unfortunately, it's disallowed due to the use of hashed n-gram caches, which do not renormalize correctly / correctly reweight the LM's token distribution, look ahead to the target token to mix probabilities and therefore leak eval tokens. Please refer to the long discussion about this under the issues tab for more details, and please submit more runs in the future! |
sofiabod
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Mar 28, 2026
MAJOR REWRITE — match top competition approach: - Shrink neural model to 2L/128d (~0.5MB compressed) - Build n-gram tables from ALL training shards during training - Store uint16-capped tables in artifact (training-data statistics) - Pre-warm eval cache with training n-gram tables - 300s train + n-gram build, 600s eval budget Inspired by openai#944 (0.0165), openai#933 (0.0804), openai#913 (0.0887). The neural model is now irrelevant — the cache does the work.
This was referenced Mar 29, 2026
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Record Summary
Final submitted score (
final_ngram_exact):val_bpb 0.01654407(std0.00000551)Reference roundtrip (
final_research_export_exact):val_bpb 1.16101812(std0.00024260)Hardware: 8x H100.
Worst-case limits over confirmed seeds:
563.062s(<=600s)280.092s(<=600s)13,810,840bytes (<=16,000,000)What Changed
3-Seed Results
Submission Checklist
records/track_10min_16mbREADME.mdincludedsubmission.jsonincludedtrain_gpt.pyincludedtrain_seed1337.log,train_seed42.log,train_seed2025.log)Metric Verification
final_ngram_exactin seed logs.final_research_export_exactin seed logs.