Experiment: Lower SCALAR_LR from 0.7 to 0.6 to test whether slower learning of per-layer residual and initial scaling parameters improves validation bpb.

Campaign

• Campaign: optimizer-tuning

Hypothesis

Lower SCALAR_LR from 0.7 to 0.6 to test whether slower learning of per-layer residual and initial scaling parameters improves validation bpb.

Parent Context

• Parent master hash: 935fdbf9f4ae8a5ef5bcb76552acea2bc5801965
• Master val_bpb at dispatch: 0.962777
• Worker id: worker-0
• Worktree: /Users/ben/code/open-autolab/.runtime/worktrees/scalar-lr-06

Single Variable

Expected Upside

Duplicate Check

Runtime

• Log path: /Users/ben/code/open-autolab/research/live/scalar-lr-06.log
• Launcher: uv run scripts/opencode_worker.py run scalar-lr-06

Allowed Edit Scope

• train.py only

Run Plan

• Refresh master with uv run scripts/refresh_master.py --fetch-dag
• Run uv run scripts/hf_job.py preflight
• Run uv run scripts/hf_job.py launch --mode experiment
• Stream logs to the reserved path
• Parse uv run scripts/parse_metric.py /Users/ben/code/open-autolab/research/live/scalar-lr-06.log

Result

• Local val_bpb: <value>
• Submitted: yes|no
• Interpretation: <one or two sentences>
• Failure mode, if any: <brief note>

Memory-Keeper Handoff

• One short note for research/notes.md: ``
• Any do-not-repeat update: ``