Non-record: Swarm-Guided KG-Conditioned Training (val_bpb=1.1220)

records/track_non_record_16mb/2026-03-29_SwarmGuided_KGConditioned_Training/README.md

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+# Swarm-Guided Training: A Multi-Agent Think Tank Directs a Tiny Model Inside the 600s/16MB Limit
+
+**Submission for OpenAI Parameter Golf (non-record / creative track)**
+
+> A multi-agent Think Tank Swarm actively steers the training of a tiny GPT model in real time — dynamically adjusting hyperparameters and using a 500K-node knowledge graph to condition embedding initialization.
+
+I designed and built this system from scratch — the swarm architecture, the knowledge graph structure, the agent roles, the decision logic, and the integration with the training loop are all my original work.
+
+Instead of a static training script, a team of 4 specialist agents (QAT Timing, KG Weight, Gradient Health, MTP Weight) uses a typed 500K-node knowledge graph to make real-time decisions about quantization timing, knowledge graph influence scheduling, gradient clipping, and multi-token prediction weighting. The swarm makes 8 guided decision cycles during training, then outputs the final 16MB artifact with a full decision log.
+
+This is the first submission (to my knowledge) where a multi-agent swarm acts as the live trainer and optimizer inside the strict Parameter Golf constraints.
+
+### Core Idea
+- The swarm is not just post-training analysis — it **steers the training loop itself**.
+- Agents observe training metrics (loss trajectories, gradient norms, LR scale, training progress) and vote on changes.
+- A 500K-node typed-edge knowledge graph (CAUSES, REQUIRES, SOLVES, CONTRADICTS) conditions the model's embedding initialization — giving semantically important tokens a head start.
+- Everything is pure Python, rule-based heuristics — no LLM calls during training. Total swarm overhead: **<300 microseconds** across 600 seconds.
+- The final model is a standard tiny GPT — the novelty is in **how** it was trained.
+
+### Key Components I Designed
+- **Think Tank Swarm** — multi-agent research system with typed-edge knowledge graph traversal, specialist agents, and voting-based consensus
+- **Typed Knowledge Graph** — 500,292 nodes, 121,084 edges with 10 edge types (Solves, Causes, Requires, Enables, Contradicts, ConnectsTo, Improves, PartOf, TradeoffOf, AlternativeTo)
+- **KG-Conditioned Embedding Init** — token embeddings scaled by PageRank importance from the knowledge graph at initialization (zero runtime cost)
+- **VotingMesh** — consensus mechanism where agents propose changes with confidence scores, applied only above threshold
+- **Swarm Decision Log** — full transparency into what the swarm decided, when, and why
+
+The TTS research swarm (running on separate infrastructure with Phi-3 + Qwen2.5-7B local models) ran two investigation missions to design this approach before any training code was written. The knowledge graph was built from a 615MB library spanning 58 domain clusters.
+
+All high-level architecture, agent roles, knowledge graph design, and system integration were mine. I used AI tools (Grok, Claude, Gemini) as implementation collaborators, but the vision and system design are original.
+
+### The 4 Swarm Agents
+
+| Agent | Observes | Controls | Decision Logic |
+|-------|----------|----------|---------------|
+| **QAT Timing** | LR scale + training progress | When to enable quantization-aware training | Fires when warmdown begins (scale < 0.15) but not before 40%. Safety deadline at 65%. |
+| **KG Weight** | Training phase | Knowledge graph influence schedule | Ramps 0.3 → 0.5 early (guide learning), holds 0.4 mid-training, tapers to 0.1 late (free convergence). |
+| **Gradient Health** | Gradient norms | Gradient clipping threshold | Tightens clipping from 0.3 to 0.15 if grad_norm > 2.0. |
+| **MTP Weight** | Training phase | Multi-token prediction loss weight | Reduces MTP from 0.1 → 0.05 after 75% to shift focus to primary loss. |
+
+### Results (Seed 1337)
+
+| Metric | Score |
+|--------|-------|
+| Pre-quant (EMA) | 1.1397 |
+| Post-quant (int6 roundtrip) | 1.1481 |
+| Sliding window (stride=64) | 1.1245 |
+| **Legal TTT** | **1.1220** |
+| Artifact size | 15,955,969 bytes (under 16MB limit) |
+| Training steps | 6,956 / 20,000 |
+| Step average | 86.27 ms |
+| Swarm cycles | 8 |
+| Swarm decisions | 2 |
+| Swarm overhead | <300 microseconds total |
+
+### Swarm Decision Log
+
+```
+Swarm: 8 cycles, 2 decisions
+  cycle 1 step 800:  kg_weight_agent  0.3 -> 0.5  (conf=0.75, 50us) progress=0.04, adjusting KG schedule
+  cycle 4 step 3200: kg_weight_agent  0.5 -> 0.4  (conf=0.75, 39us) progress=0.16, adjusting KG schedule
+```
+
+### Architecture
+
+```
+                    ┌─────────────────────────────┐
+                    │  Knowledge Graph (500K nodes) │
+                    │  10 typed edge types           │
+                    │  121K edges, 58 clusters        │
+                    └──────────┬──────────────────┘
+                               │ PageRank → 358 token
+                               │ importance scores (976 bytes)
+                               ▼
+┌──────────────────────────────────────────────────┐
+│              Embedding Initialization             │
+│  tok_emb.weight[tid] *= 1 + 0.1 * importance     │
+│  (one-time, zero runtime cost)                    │
+└──────────────────────────────────────────────────┘
+                               │
+                    ┌──────────▼──────────┐
+                    │   Training Loop      │
+                    │   (600s, 8xH100)     │
+                    └──────────┬──────────┘
+                               │ Every 800 steps
+                    ┌──────────▼──────────┐
+                    │   Swarm Decision     │
+                    │   Cycle (~50 μs)     │
+                    │                      │
+                    │  ┌── QAT Timing ───┐ │
+                    │  ├── KG Weight ────┤ │
+                    │  ├── Grad Health ──┤ │
+                    │  └── MTP Weight ───┘ │
+                    │                      │
+                    │  Observe → Vote →    │
+                    │  Apply (if conf≥0.6) │
+                    └──────────┬──────────┘
+                               │
+                    ┌──────────▼──────────┐
+                    │   Final Model +      │
+                    │   Decision Log        │
+                    └─────────────────────┘
+```
+
+### Base Stack
+
+The swarm layers on top of the proven Parameter Golf recipe:
+- 11 layers, 512d, 8 heads, 4 KV heads, 3x MLP
+- LeakyReLU(0.75)² activation
+- Parallel Muon optimizer
+- Multi-Token Prediction (2 heads, weight=0.1)
+- EMA weight averaging (0.997)
+- BigramHash (2048) + SmearGate
+- XSA (last 4 layers) + Partial RoPE + LN Scale
+- Int6 quantization (GPTQ-lite + LZMA)
+- Legal Score-First TTT
+- Sliding window evaluation (stride=64)
+
+### Why This Matters
+
+Most submissions optimize a fixed training script. This submission shows a swarm can **dynamically steer** training decisions inside the tight constraints — opening the door to agentic self-improvement at the competition scale.
+
+The knowledge graph provides external semantic structure that a 28M-parameter model cannot learn from data alone. By biasing embeddings toward important concepts, the model allocates capacity more efficiently from the start.
+
+This is a proof-of-concept for **agentic training** — where the training process is not a static script but an intelligent system that observes, decides, and adapts.
+
+### Files
+
+| File | Size | Purpose | Counted in artifact? |
+|------|------|---------|---------------------|
+| `train_gpt.py` | 94KB | Training script + swarm integration | Yes |
+| `swarm_agents.py` | 11KB | 4 agents + VotingMesh | No (imported) |
+| `kg_data.py` | 1KB | Compressed KG importance data | No (imported) |
+| `submission.json` | <1KB | Metadata | No |
+
+### Reproducibility
+
+```bash
+LATE_QAT_THRESHOLD=0 TTT_ENABLED=1 KG_LOSS_WEIGHT=0.1 SEED=1337 \
+  torchrun --nproc_per_node=8 train_gpt.py
+```
+
+Requires `swarm_agents.py` and `kg_data.py` in the same directory as `train_gpt.py`.
+
+### Why This Matters to OpenAI
+
+This submission demonstrates agentic self-improvement inside the strict 600s/16MB constraints — a step toward training systems that can reason about their own training process. It showcases a complete stack: multi-agent orchestration, typed knowledge graphs, rule-based consensus, and reliable execution under real production constraints.
+
+**Built while competing in OpenAI Parameter Golf (March 2026).**

records/track_non_record_16mb/2026-03-29_SwarmGuided_KGConditioned_Training/kg_data.py

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+"""Auto-generated KG importance data. Do not edit."""
+KG_IMPORTANCE_B64 = "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"

records/track_non_record_16mb/2026-03-29_SwarmGuided_KGConditioned_Training/submission.json

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+{
+  "author": "michaelwinczuk",
+  "github_id": "michaelwinczuk",
+  "val_bpb": 1.1220,
+  "approach": "Swarm-Guided KG-Conditioned Training",
+  "description": "A multi-agent swarm (4 rule-based agents, <300us total overhead) makes training decisions via consensus voting while a 500K-node knowledge graph conditions embedding initialization. Novel agentic training approach.",
+  "track": "non_record",
+  "seed": 1337,
+  "hardware": "8xH100 SXM",
+  "artifact_bytes": 15955969,
+  "training_time_ms": 600070,
+  "steps": 6956,
+  "step_avg_ms": 86.27
+}