[Non-record] Meta-Learned TTT + Error-Guided Adaptation Analysis (val_bpb=1.1645)#294
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sseanliu wants to merge 12 commits intoopenai:mainfrom
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[Non-record] Meta-Learned TTT + Error-Guided Adaptation Analysis (val_bpb=1.1645)#294sseanliu wants to merge 12 commits intoopenai:mainfrom
sseanliu wants to merge 12 commits intoopenai:mainfrom
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…x 4 recycled = 12 effective layers Architecture: 3 unique blocks at dim=768 (12 heads, 6 KV heads) recycled 4x each for 12 effective layers with per-iteration scale/mix params and U-Net skip connections. 13.2M unique params in ~12MB compressed (3.9MB headroom vs 16MB cap). 50% wider representations + 20% more effective depth vs SOTA's 10x512.
- Extract _apply_block helper for cleaner per-iteration logic - Remove _block_for_layer, use direct modular indexing - Reduce code from 1325 to 1321 lines
Three files: - program.md: Instructions for the AI agent (experiment loop, logging, directions) - prepare.py: Fixed utilities (data loading, evaluation, quantization, size checking) - train.py: Modifiable baseline (SOTA architecture, the only file the agent edits) Based on Karpey's autoresearch framework, adapted for parameter-golf constraints (16MB artifact limit, fixed FineWeb dataset, SentencePiece 1024 vocab).
…o MetaTTT submission
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Summary
Non-record research submission exploring test-time adaptation strategies for compressed language models at 16MB scale.
Key findings
Score
Methodology
Files
records/track_10min_16mb/2026-03-20_MetaTTT_v2/train_gpt.py— Training with Reptileeval_error_guided_ttt.py— Error-guided TTT evaluationrecords/track_10min_16mb/2026-03-20_MetaTTT_v2/README.md— Full analysisrecords/track_10min_16mb/2026-03-20_MetaTTT_v2/submission.json— MetadataSee README for detailed methodology, results, and theoretical context.