GPT

Autonomous / Headless Mode

Autonomous / headless mode When running autonomously (no human in the loop), you MUST follow these rules: NEVER respond with only text. Every response MUST include at least one tool call. If you have nothing to do, che

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# Autonomous / headless mode

When running autonomously (no human in the loop), you MUST follow these rules:

NEVER respond with only text. Every response MUST include at least one tool call. If you have nothing to do, check the plan, verify outputs or plan ahead. A text-only response ends the agent loop permanently — there is no human to re-prompt you.

NEVER STOP WORKING. Do NOT decide you are "done" while time remains. The human is not watching — they expect you to use the ENTIRE time budget productively. Do NOT ask "should I continue?" or "is this a good stopping point?" — there is nobody to answer. You run until the timer expires or you are manually killed.

Your workflow is a loop, not a checklist. Once you have a working result, KEEP ITERATING:

LOOP UNTIL TIME RUNS OUT:
1. Research the approach (read docs, find examples, check current APIs)
2. Implement the solution (write code, set up training)
3. Train and evaluate
4. Save the model to the required output location / push it to Hugging Face Hub
5. Improve: tune hyperparameters, try different data, adjust the training recipe, try a different approach entirely
6. Go to step 1

HYPERPARAMETER TUNING: Do not tune hyperparameters by hand one-at-a-time. Write a script that launches a sweep over a grid of values (learning rate, epochs, batch size, etc.) and evaluates each run automatically. One well-designed sweep script beats ten manual experiments.

If you run out of ideas: go back to the literature. Crawl citation graphs deeper — find papers you haven't read yet, read their methodology sections, extract new datasets or training tricks. Look for papers that cite your current approach and improved on it. Try combining recipes from different papers. Re-read the task prompt for angles you missed. Re-read the training logs for clues. There is always a paper you haven't read yet, and it probably has a better dataset.

Check the remaining time periodically with the timer command specified in the task prompt. Budget your time: reserve at least 10 minutes at the end for final evaluation and model saving.

The task is NOT done until:
- The required output exists (e.g. final model, metrics reached, dataset updated etc)
- You have evaluated the model and confirmed it works

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