Mentor Meeting Playbook — LifeStack Engine
The Core Framing
Research Question: "Can a small model (1.5B) learn to navigate multi-domain, causally-coupled crises better than a base LLM, using GRPO with a 7-day horizon reward?"
Slide Deck Structure (8 Slides Max)
Slide 1 — The Gap (30 sec)
- Current AI: Single-turn advice, no state, no consequence modeling.
- LifeStack: Life as a Markov Decision Process — 23 metrics, 6 domains, 40 causal edges.
- Hook: "We built the environment that lets you train models on the 'ripple effects' of human decisions."
Slide 2 — The Environment (1 min)
- Standards-Based: LifeStackEnv extends
openenv.Environment. - Causal Foundation: 40 edges from Starcke & Brand (2012) — research-grounded, not arbitrary.
- Deterministic World:
DependencyGraph.propagate()uses matrix math, not LLM hallucination. - State Vector: 26-dim observation space across 23 tracked metrics.
Slide 3 — The Cascade (The Visual Hook)
- Visual: Screenshot/GIF of the 4-frame cascade animation (STABLE → DISRUPTION → 1ST CASCADE → 2ND CASCADE).
- Narrative: "A $350 flight rebooking cascades into stress (day 1) → sleep loss (day 2) → relationship strain (day 4). Our graph engine computes this propagation."
Slide 4 — Training Setup (45 sec)
- Model: Qwen2.5-1.5B-Instruct, fine-tuned with GRPO via HuggingFace TRL.
- Reward: 7-signal orchestrator (Milestone, Outcome, Preservation, Replan, Efficiency, Reasoning Coherence).
- Innovation: $\gamma=0.9$ discounted 7-day rollout. Decisions are penalized today if they cause system collapse on day 4.
Slide 5 — The Research Result (Comparison)
| Feature | Untrained LLM (Base) | GRPO-Trained LifeStack |
|---|---|---|
| Logic | Treats each action independently | Reasons across all 6 domains |
| Budgeting | Maximizes single metric | Preserves global resource budget |
| Strategy | Generic advice | Reward-shaped justification |
| Memory | None | RAG memory flywheel (+116% efficiency) |
Slide 6 — Memory Flywheel
- The Numbers: Cold start 42% success rate → Warm (RAG) 88% success rate.
- The Edge: ChromaDB retrieval lets the agent reason from past successful precedents.
Slide 7 — Current Progress (Status)
- Live: Flask demo on HuggingFace Spaces.
- Functionality: 6 working tabs including Comparison, Personality Lab, and What-If Lab.
- Pipeline: GRPO training backbone complete; model lazy-loads for instant demo reliability.
Slide 8 — Next Steps
- Full Multi-Step Evaluation: Running 30-day episodes (beyond single-action).
- Real Data Ingestion: OAuth for Gmail/Calendar signals (currently stubbed).
- Quantitative Scaling: Benchmarking 1000+ synthetic scenarios.
Demo Script (The 4-Step Sequence)
- Stage the Crisis: Open the "Situational Portal". Select Alex (Executive) + Career crisis.
- The Cascade: Hit "Start Simulation". Let the 4-frame animation play. Silence for 5 seconds. Then: "Every color change was computed by the graph, zero LLM involvement yet."
- The Heatmap: Point at the Red cells. "Red means crisis. Notice how a work deadline dragged Physical Health into the red. The agent must now resolve this composite state."
- The Comparison: Switch to "Trained vs Untrained". Hit "Run Comparison". "On the left is the raw model. On the right is the model after RL feedback on our 7-day reward signal."
Counter-Questions & Defensive Positioning (QA)
| Question | Winning Answer |
|---|---|
| "Is this just prompt engineering?" | "No. We modified model weights via GRPO. The reward comes from the environment simulator, not a system prompt." |
| "Your environment is hand-coded?" | "The environment physics are expert-coded (research-based); the policy navigating them is learned. Chess rules are coded, but AlphaZero is a research breakthrough." |
| "How do you prevent reward hacking?" | "Triple-check: Reasoning audit, resource preservation costs, and discounted 7-day rollouts penalize short-sighted wins." |
| "Why 1.5B parameters?" | "Intentional. It allows consumer-local deployment (privacy) and makes the RL training signal highly measurable." |
The Perfect Hook
Opening (30 Seconds)
"Most AI tools give you advice. LifeStack gives you consequences. We built a 6-domain, 23-metric RL environment where a career crisis cascades into sleep loss, relationship strain, and financial pressure—all causally linked. Then we trained a model to navigate that using GRPO. The question we're answering is: can a 1.5B model, trained on life-state rewards, make better long-term decisions than an untrained LLM? We can show you the delta right now."
Closing (The Final Word)
"The real contribution isn't the UI—its the environment + training loop. Everything you see in the demo is an artifact of that system working."