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refactor: rename teacher/student to optimizer/target, remove best skills, fix slow update

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- Rename teacher -> optimizer, student -> target across all code, configs, docs, prompts
- CLI: --teacher_model -> --optimizer_model, --student_model -> --target_model
- Remove best_skill files, keep only initial skills
- Fix slow update gate (force write into skill)
- Fix SLOW_UPDATE marker stripping
- Remove deep_reflect and meta_reflect mechanisms
- Update .env.example with export prefix and azure_cli docs
- Add endpoint empty validation in azure_openai.py

Co-authored-by: Copilot <223556219+Copilot@users.noreply.github.com>
This commit is contained in:
Cuzyoung
2026-05-24 19:15:03 +00:00
parent 6e165d5347
commit 4a1b984d87
70 changed files with 1083 additions and 2068 deletions
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docs/guide/dl-analogy.md
+4 -4
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@@ -7,9 +7,9 @@ SkillOpt is designed around a core insight: **optimizing natural-language prompt
| Deep Learning | SkillOpt | Description |
|---|---|---|
| **Model weights** | Skill document (Markdown) | The thing being optimized |
| **Forward pass** | Rollout | Student executes tasks using current skill |
| **Forward pass** | Rollout | Target executes tasks using current skill |
| **Loss function** | Task evaluator | Scores task execution quality |
| **Backpropagation** | Reflect | Teacher analyzes failures → edit patches |
| **Backpropagation** | Reflect | Optimizer analyzes failures → edit patches |
| **Gradients** | Edit patches | Proposed changes to the skill |
| **Gradient aggregation** | Patch aggregation | Merge similar edits |
| **Gradient clipping** | Edit selection | Cap max edits per step |
@@ -21,7 +21,7 @@ SkillOpt is designed around a core insight: **optimizing natural-language prompt
| **Training step** | Step | One rollout → reflect → update cycle |
| **Epoch** | Epoch | Full pass with slow update + meta memory |
| **Momentum** | Slow update | Longitudinal comparison at epoch boundary |
| **Meta-learning** | Meta skill | Cross-epoch teacher strategy memory |
| **Meta-learning** | Meta skill | Cross-epoch optimizer strategy memory |
| **Batch size** | `batch_size` | Tasks sampled per rollout |
| **Data parallelism** | `analyst_workers` | Parallel reflection workers |
| **Training set** | Train split | Items used for rollout |
@@ -44,7 +44,7 @@ From our experiments, these DL intuitions transfer well:
- **Cosine schedule > constant** — same as in DL, cosine annealing helps convergence
- **Moderate LR (4-16) > very high/low** — too few edits = slow learning, too many = noisy
- **Slow update helps** — longitudinal comparison prevents catastrophic forgetting across epochs
- **Meta skill memory improves reflection** — teacher benefits from cross-epoch strategy notes
- **Meta skill memory improves reflection** — optimizer benefits from cross-epoch strategy notes
!!! warning "What doesn't transfer"
- **Batch size ≠ better** — larger rollout batches have diminishing returns due to API costs