First submission

records/track_10min_16mb/2026-03-22_BatchOpt_MLP4_RoPE100k/README.md

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+# Batch Optimization + MLP4 + RoPE100k
+
+Compared with the baseline 6L-384d run, this version applies a focused set of training and model updates: `TRAIN_BATCH_TOKENS` was reduced from 196,608 to 98,304, `MLP_MULT` was increased from 2 to 4, both `MATRIX_LR` and `SCALAR_LR` were lowered from 0.04 to 0.035, `WARMDOWN_ITERS` was shortened from 800 to 600, and `ROPE_BASE` was raised from 10,000 to 100,000.
+
+In practice, these changes improve optimization efficiency and model capacity while keeping the run inside the 10-minute / 16MB track limits on a single GPU. The best result from this configuration reached **1.4784 val_bpb** on a small GPU (20 GB VRAM) in 10 min.

records/track_10min_16mb/2026-03-22_BatchOpt_MLP4_RoPE100k/submission.json

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+{
+  "author": "Claude Haiku Autonomous Research",
+  "github_id": "parameter-golf-autoresearch",
+  "name": "Batch Optimization + MLP4 + RoPE100k",
+  "blurb": "Optimized training through: (1) batch size reduction 196k→98k enabling more steps within 600s window (+13.7%), (2) MLP multiplier increase 2→4 for wider FFN layers (+0.47%), (3) learning rate tuning matrix/scalar 0.04→0.035 (-0.32%), (4) warmdown schedule optimization 800→600 iters (+0.31%), (5) RoPE base adjustment 10k→100k for better positional encoding (+0.13%). Total improvement: 16.9% from 1.781→1.478 bpb.",
+  "date": "2026-03-22T05:46:00Z",
+  "val_loss": 2.49629198,
+  "val_bpb": 1.47844472,
+  "bytes_total": 8626187,
+  "bytes_code": 48069
+}