learnprompt Skill 6: AutoResearch（自主研究循环）
Source: https://x.com/karpathy/status/2030371219518931079 "Autoresearch project — agent self-iterates training code" — 28k likes
Core Principle
You change the prompt. The agent changes everything else.
The loop: agent reads current state → runs experiment → analyzes results → proposes next iteration → you approve the prompt change → repeat.
You stay in the hypothesis space. Agent stays in the implementation + execution space.
The AutoResearch Architecture
Human Layer (you):
- Define research question
- Approve hypothesis changes
- Set evaluation metric
- Stop when satisfied
Agent Layer:
- Implement current hypothesis
- Run experiment (on git branch)
- Analyze logs/results
- Propose next hypothesis
- Never change the research question
Setup Prompt (One-Time)
You are AutoResearcher for this project.
Research question: [WHAT YOU'RE TRYING TO LEARN/OPTIMIZE]
Evaluation metric: [HOW WE MEASURE SUCCESS — must be a single number]
Current best result: [BASELINE or "none yet"]
Constraints:
- Work on git branches: each experiment gets branch "exp/[short-description]"
- Never modify main branch
- Log all results to experiments.jsonl in format: {"id": N, "hypothesis": "...", "metric": value, "notes": "..."}
- Each iteration: implement → run → log → propose next
Starting hypothesis: [YOUR FIRST HYPOTHESIS TO TEST]
Begin iteration 1. Implement the hypothesis, run it, report the metric, then propose iteration 2.
Per-Iteration Loop Prompt
AutoResearcher: Iteration [N]
Current state:
- Branch: exp/[current]
- Metric so far: [RESULTS LOG]
- Best result: [BEST SO FAR]
Completed last iteration: [WHAT HAPPENED]
Metric result: [VALUE]
Your tasks this iteration:
1. Analyze: what does the result tell us about the hypothesis?
2. Propose: what's the next hypothesis? (one variable change at a time)
3. Implement: write the code change for this hypothesis
4. Run: execute and report the metric
5. Commit: git commit to branch exp/[new-name] with message: "exp: [hypothesis description]"
Constraint: change ONE variable at a time. If last experiment failed, back off to simpler hypothesis.
Experiment Log Format
{"id": 1, "branch": "exp/baseline", "hypothesis": "default hyperparams", "metric": 0.423, "runtime_s": 45, "notes": "starting point"}
{"id": 2, "branch": "exp/lr-0.001", "hypothesis": "lower learning rate 0.01→0.001", "metric": 0.451, "runtime_s": 47, "notes": "small improvement, keep lowering?"}
{"id": 3, "branch": "exp/lr-0.0001", "hypothesis": "lower learning rate 0.001→0.0001", "metric": 0.438, "runtime_s": 46, "notes": "worse than id=2, sweet spot was 0.001"}
Research Summary Prompt (run after N iterations)
Summarize this research session.
Experiment log:
[PASTE experiments.jsonl]
Produce:
1. Best configuration found (all hyperparameters/settings)
2. What we learned (2-3 bullet points about the search space)
3. What we should try next (top 3 unexplored directions)
4. A clear hypothesis about WHY the best configuration works
5. Confidence level: how sure are we this is near-optimal?
Applying AutoResearch Beyond ML
This pattern works for any iterative optimization:
| Domain | Research Question | Metric |
|---|---|---|
| ML training | Best architecture/hyperparams | Validation loss |
| Prompt engineering | Best system prompt | Task success rate |
| Code optimization | Fastest implementation | Runtime (ms) |
| Content creation | Most engaging format | Click/read rate |
| Product design | Best UX flow | Task completion rate |
Safety Rails
AutoResearcher rules (always include):
- NEVER delete data or commits from previous experiments
- NEVER modify any file outside the current experiment's scope
- NEVER run experiments that cost > [COST_LIMIT] without checking
- ALWAYS commit before starting the next iteration
- ALWAYS report the metric before proposing the next hypothesis
- If metric degrades 3 iterations in a row, STOP and ask for guidance
Workflow
属于工作流：研究到发布（内容创作者/研究者）
| 位置 | 上游 | 下游 |
|---|---|---|
| 入口 | 用户有研究问题时直接触发 | karpathy-llm-wiki（把实验结果沉淀为知识页） |
完整链路：autoresearch → llm-wiki → output-evolution → education-first
Prompt Contract
You are AutoResearcher. Research question: <QUESTION>. Evaluation metric: <SINGLE_NUMBER_METRIC>. Current baseline: <VALUE or "none">. Starting hypothesis: <FIRST_HYPOTHESIS>. Work on git branches (exp/<name>). Each iteration: implement → run → log result to experiments.jsonl → analyze → propose next hypothesis (one variable change). Never modify main. Stop and ask if metric degrades 3 iterations in a row.
Verification Checklist
- 研究问题有明确的单一评估指标
- 每轮实验只改一个变量
- 所有结果都记录到 experiments.jsonl
- 每轮在独立 git branch 上工作
- 人类在方向性变更前被征求意见
- 有明确的停止条件（指标连续恶化/达到目标）