4a1b984d87
- 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>
3.1 KiB
Deep Learning ↔ SkillOpt Analogy
SkillOpt is designed around a core insight: optimizing natural-language prompts follows the same structure as training neural networks. This page maps every DL concept to its SkillOpt counterpart.
Complete Mapping
| Deep Learning | SkillOpt | Description |
|---|---|---|
| Model weights | Skill document (Markdown) | The thing being optimized |
| Forward pass | Rollout | Target executes tasks using current skill |
| Loss function | Task evaluator | Scores task execution quality |
| 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 |
| Learning rate | learning_rate |
Max number of edits applied per step |
| LR scheduler | lr_scheduler |
Decay schedule: cosine, linear, constant |
| SGD step | Skill update | Apply selected patches to document |
| Validation set | Selection split | Gate checks improvement before accepting |
| Early stopping | Gate patience | Reject updates that don't improve |
| 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 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 |
| Test set | Test split | Held-out final evaluation |
| Warm-up | (implicit) | High LR early steps explore broadly |
| Checkpointing | Skill snapshots | Saved after each accepted step |
| Transfer learning | Seed skill / cross-benchmark init | Start from pre-trained skill |
Why This Analogy Matters
- Familiar mental model: ML practitioners immediately understand how to tune SkillOpt
- Principled hyperparameter search: Grid search over
learning_rate×lr_schedulerworks just like in DL - Proven mechanisms: Gating ≈ validation-based selection, patience ≈ early stopping, slow update ≈ momentum — all with strong theoretical motivation
Hyperparameter Transfer Rules
From our experiments, these DL intuitions transfer well:
!!! success "What transfers" - 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 — 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 - More epochs ≠ better — skills converge faster than neural networks (2-4 epochs usually enough)