log

log

Task

After each completed iteration, append one new entry to task/experiment_log.md.

Rules

• If task/experiment_log.md does not exist, create it. If it already exists, append a new entry at the end.
• Each call should record only the iteration that has just finished.
• Organize the log by stage, and record the work done in the current iteration under the relevant stage.

Entry Format

Organize entries by stage when a stage is available. If the relevant stage heading does not exist, create it.

Use this Markdown format for each new entry:

Iteration :

• Context: <stage, objective, or current focus>
• Status: completed | failed | blocked
• Motivation:
• References: <papers, docs, repos, datasets, blogs, or notes consulted; write "None" if not used>
• Starting checkpoint: <base model, previous checkpoint, or final_model path used as training start>
• Training data: <datasets/files used, sizes, filters, construction method, validation notes>
• Method: <training method, recipe, prompt/data strategy, or implementation changes>
• Training config: <key hyperparameters, command, epochs, lr, batch size, LoRA/full fine-tune, etc.>
• Evaluation: <evaluation command, benchmark split, limit/full setting, metric>
• Result: <exact score, failure, or observed behavior>
• Analysis: <what changed, what likely caused it, whether the hypothesis was supported>
• Artifacts: <model path, logs, data files, checkpoints>
• Next action:

Rules:

• Fill every field. Use None or N/A only when the field truly does not apply.
• Record concrete evidence, not vague summaries.
• Include exact metrics, commands, paths, and dataset sizes when available.
• If references were consulted, record enough detail to identify them later.
• If the iteration failed or was blocked, record the specific cause.
• The next action must follow from the recorded result and analysis.