wehub-resource-sync
52 lines
3.1 KiB
Markdown
52 lines
3.1 KiB
Markdown
# Deep Learning ↔ SkillOpt Analogy
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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.
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## Complete Mapping
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| Deep Learning | SkillOpt | Description |
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|---|---|---|
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| **Model weights** | Skill document (Markdown) | The thing being optimized |
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| **Forward pass** | Rollout | Target executes tasks using current skill |
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| **Loss function** | Task evaluator | Scores task execution quality |
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| **Backpropagation** | Reflect | Optimizer analyzes failures → edit patches |
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| **Gradients** | Edit patches | Proposed changes to the skill |
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| **Gradient aggregation** | Patch aggregation | Merge similar edits |
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| **Gradient clipping** | Edit selection | Cap max edits per step |
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| **Learning rate** | `learning_rate` | Max number of edits applied per step |
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| **LR scheduler** | `lr_scheduler` | Decay schedule: cosine, linear, constant |
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| **SGD step** | Skill update | Apply selected patches to document |
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| **Validation set** | Selection split | Gate checks improvement before accepting |
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| **Early stopping** | Gate patience | Reject updates that don't improve |
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| **Training step** | Step | One rollout → reflect → update cycle |
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| **Epoch** | Epoch | Full pass with slow update + meta memory |
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| **Momentum** | Slow update | Longitudinal comparison at epoch boundary |
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| **Meta-learning** | Meta skill | Cross-epoch optimizer strategy memory |
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| **Batch size** | `batch_size` | Tasks sampled per rollout |
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| **Data parallelism** | `analyst_workers` | Parallel reflection workers |
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| **Training set** | Train split | Items used for rollout |
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| **Test set** | Test split | Held-out final evaluation |
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| **Warm-up** | (implicit) | High LR early steps explore broadly |
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| **Checkpointing** | Skill snapshots | Saved after each accepted step |
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| **Transfer learning** | Seed skill / cross-benchmark init | Start from pre-trained skill |
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## Why This Analogy Matters
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1. **Familiar mental model**: ML practitioners immediately understand how to tune SkillOpt
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2. **Principled hyperparameter search**: Grid search over `learning_rate` × `lr_scheduler` works just like in DL
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3. **Proven mechanisms**: Gating ≈ validation-based selection, patience ≈ early stopping, slow update ≈ momentum — all with strong theoretical motivation
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## Hyperparameter Transfer Rules
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From our experiments, these DL intuitions transfer well:
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!!! success "What transfers"
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- **Cosine schedule > constant** — same as in DL, cosine annealing helps convergence
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- **Moderate LR (4-16) > very high/low** — too few edits = slow learning, too many = noisy
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- **Slow update helps** — longitudinal comparison prevents catastrophic forgetting across epochs
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- **Meta skill memory improves reflection** — optimizer benefits from cross-epoch strategy notes
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!!! warning "What doesn't transfer"
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- **Batch size ≠ better** — larger rollout batches have diminishing returns due to API costs
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- **More epochs ≠ better** — skills converge faster than neural networks (2-4 epochs usually enough)
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