| # God-Level Expert Architecture: Asymmetric MoE |
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| ## 🎯 Philosophy: Size Where It Matters |
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| **NOT**: Bigger model everywhere |
| **YES**: Surgical sizing for reasoning-heavy experts |
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| --- |
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| ## 📊 Asymmetric Expert Sizing (Data-Driven) |
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| ### Tier 1: Reasoning Experts (50-60MB) |
| **Math Expert** (60MB) |
| - Multi-step chain reasoning |
| - Tool verification |
| - Step-by-step validation |
| - **Why 60MB**: Handles complex reasoning chains (GSM8K 95%+ target) |
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| **Logic Expert** (60MB) |
| - Assumption tracking |
| - Implication chains |
| - Contradiction detection |
| - **Why 60MB**: Structure > lookup, needs depth |
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| **Code Expert** (50MB) |
| - Pattern stability |
| - Schema enforcement |
| - Template following |
| - **Why 50MB**: Balance between patterns and constraints |
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| ### Tier 2: Standard Experts (20-30MB) |
| All others: QA, Grammar, Sentiment, NER, etc. |
| - **Why 20-30MB**: Pattern recognition sufficient |
| - No multi-step reasoning needed |
| - Diminishing returns beyond this |
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| --- |
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| ## 🔧 Architecture Changes |
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| ### Current (All ~1.26MB) |
| ``` |
| All experts: 331K params → 1.26MB |
| ``` |
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| ### Enhanced (Asymmetric) |
| ``` |
| Math: 15M params → 60MB (47x larger) |
| Logic: 15M params → 60MB (47x larger) |
| Code: 12M params → 50MB (39x larger) |
| Others: 5-7M params → 20-30MB (15-23x larger) |
| ``` |
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| ### Model Architecture for 60MB Expert |
| ```python |
| MathExpert( |
| input_dim=256, # From shared encoder |
| hidden_dim=2048, # 8x larger (was 256) |
| num_layers=12, # 4x deeper (was 3) |
| dropout=0.1, |
| num_classes=1 # Binary validation |
| ) |
| # Params: ~15M → 60MB |
| ``` |
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| --- |
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| ## 🎓 Training Strategy (CRITICAL) |
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| ### 1. Hard Curriculum Learning |
| ``` |
| Week 1: Easy (basic ops, single-step) |
| Week 2: Medium (2-3 steps, simple chains) |
| Week 3: Hard (multi-step, edge cases) |
| Week 4: Adversarial (failures, near-misses) |
| ``` |
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| ### 2. Failure-Driven Training |
| **Include in datasets**: |
| - ❌ Wrong solutions (with corrections) |
| - ❌ Near-misses (subtle bugs) |
| - ❌ Incorrect proofs (with refutations) |
| - ❌ Off-by-one errors |
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| **Why**: Experts learn MORE from failures than perfection |
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| ### 3. Output Schemas (Non-Negotiable) |
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| **Math Expert Schema**: |
| ```json |
| { |
| "tool_used": "calculator|cas|python", |
| "tool_result": "56088", |
| "verification": "correct|incorrect", |
| "explanation": "brief step outline", |
| "confidence": 0.95 |
| } |
| ``` |
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| **Logic Expert Schema**: |
| ```json |
| { |
| "premises": ["A", "B"], |
| "inference": "C", |
| "conclusion": "valid|invalid", |
| "reasoning": "step-by-step", |
| "confidence": 0.92 |
| } |
| ``` |
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| **Code Expert Schema**: |
| ```json |
| { |
| "code": "def foo(): ...", |
| "language": "python", |
| "follows_template": true, |
| "explanation": null, // NO PROSE |
| "confidence": 0.88 |
| } |
| ``` |
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| ### 4. Refusal Training (THE SECRET) |
| **God-level = knowing when NOT to answer** |
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| Train on: |
| ``` |
| Q: "Prove Riemann hypothesis" |
| A: "REFUSE: Requires specialist mathematician, not computable" |
| |
| Q: "Calculate 123456789 * 987654321" |
| A: "TOOL: Delegating to calculator [result: 121932631112635269]" |
| |
| Q: "Is this proof valid? [complex logic]" |
| A: "UNCERTAIN: Requires formal verification tool" |
| ``` |
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| --- |
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| ## 🔄 Tool-First Architecture (Math Example) |
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| ``` |
| User: "Calculate 123 * 456" |
| ↓ |
| Router: MATH_CALC detected |
| ↓ |
| Calculator Tool: 56088 |
| ↓ |
| Math Expert: { |
| "tool_used": "calculator", |
| "result": "56088", |
| "verification": "correct", |
| "confidence": 1.0 |
| } |
| ↓ |
| Output: "The answer is 56088" |
| ``` |
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| **Math expert NEVER computes, only verifies/explains** |
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| --- |
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| ## 📈 Expected Performance (Benchmarked) |
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| | Expert | Size | Target Accuracy | Benchmark | |
| |--------|------|----------------|-----------| |
| | Math | 60MB | 95%+ | GSM8K-like | |
| | Logic | 60MB | 90%+ | Logical reasoning | |
| | Code | 50MB | 85%+ | HumanEval-style | |
| | Grammar | 25MB | 80%+ | CoLA | |
| | Others | 20-30MB | 70%+ | Domain-specific | |
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| --- |
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| ## ⚠️ What NOT to Do |
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| ❌ **Don't increase mother model** (keep OLMoE sparse) |
| ❌ **Don't overlap responsibilities** (specialization key) |
| ❌ **Don't trust LLM math** (always tool-first) |
| ❌ **Don't let experts ramble** (schemas enforce) |
| ❌ **Don't train on perfect data only** (failures teach more) |
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| --- |
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| ## 🚀 Implementation Plan |
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| ### Phase 1: Resize Key Experts |
| 1. Math Expert: 1.26MB → 60MB |
| 2. Logic Expert: 1.26MB → 60MB |
| 3. Code Expert: 1.26MB → 50MB |
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| ### Phase 2: Enhanced Training |
| 1. Curriculum datasets (easy → hard) |
| 2. Failure examples (wrong → correct) |
| 3. Output schema enforcement |
| 4. Refusal training |
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| ### Phase 3: Tool Integration |
| 1. Math → Calculator/CAS first |
| 2. Logic → Expert only (high confidence) |
| 3. Code → Schema + templates |
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| ### Phase 4: Validation |
| 1. GSM8K-style math eval (95%+ target) |
| 2. Logic reasoning tests (90%+ target) |
| 3. Code generation (85%+ target) |
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| --- |
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| ## 💡 The Real Secret |
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| **God-level behavior = Architecture + Discipline + Refusal** |
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| NOT just bigger models. |
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| You already have: |
| - ✅ Right architecture (hybrid MoE) |
| - ✅ Right router (ISL-based) |
| - ✅ Right discipline (task contracts) |
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| Now add: |
| - 🔄 Asymmetric sizing (surgical) |
| - 🎓 Curriculum training (hard) |
| - 🛡️ Refusal training (intelligent) |
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| **This is how you build god-level experts on CPU.** 🧠🔥 |
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