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| # Viraltest v2 — Pitch Deck Outline (8 slides) | |
| ## Slide 1: Title | |
| - **Viraltest v2: Teaching LLMs World Modeling Through Instagram Strategy** | |
| - Theme #3.1 — Professional Tasks | |
| - OpenEnv Hackathon India 2026 | |
| - Team: [your team name] | |
| ## Slide 2: The Problem | |
| - $250B creator economy, 67M creators (Goldman Sachs 2025) | |
| - 73% experience burnout; Instagram drives 88% of it (Awin 2024) | |
| - Algorithm changes constantly — no one tells you the rules | |
| - Existing tools show analytics but don't teach strategy | |
| - **Gap:** No RL environment captures this tradeoff with realistic dynamics | |
| ## Slide 3: The World | |
| - 30-day Instagram simulation (monthly cycle) | |
| - Mosseri-aligned signals: watch_time, sends, saves, likes (official Jan 2025) | |
| - Hour-by-hour heatmap (Buffer 9.6M + Sprout 2B) | |
| - 7 competitor archetypes, 5 audience segments, ~120 tags | |
| - Piecewise-linear sleep model (Van Dongen 2003, *Sleep*) | |
| - Tiered audience fatigue (Buffer 2.1M) | |
| ## Slide 4: The Tools (Theme #3.1 Fit) | |
| - Agent starts with SPARSE observation (energy, followers, reward) | |
| - 8 discoverable tools: query_trends, query_competitor, query_audience, query_tag_history, predict_engagement, draft_review, query_creator_pool, propose_collab | |
| - API budget (100/episode) — can't query everything, must prioritize | |
| - Notes field for hypothesis tracking across days | |
| - Counterfactual coach: "here's what would have happened with optimal timing" | |
| ## Slide 5: Training Pipeline | |
| - TRL GRPO on Qwen2.5-1.5B-Instruct (free Colab T4) | |
| - Reward: per-step env reward + 2× terminal grader score | |
| - 200 episodes, batch 4, 50 GRPO steps | |
| - 3 tasks: monthly_engage → monthly_strategic → monthly_competitive | |
| - Multi-episode chain: brand state persists across months | |
| ## Slide 6: Results | |
| - [Embed reward_curve.png — ascending curve over training] | |
| - [Embed before_after.png — smart baseline vs trained agent per task] | |
| - Trained agent: uses tools on day 1, adapts strategy by day 5, manages energy throughout | |
| - Score improvement on monthly_competitive: [X% → Y%] | |
| ## Slide 7: Sources & Verifiability | |
| - 4-tier source quality bar (peer-reviewed → industry → official → survey) | |
| - 7 Tier-1 papers, 9 Tier-2 studies, 1 Tier-3 official statement | |
| - Every constant has a DOI/PMID/arXiv ID | |
| - Tier-5 SEO blogs explicitly rejected (13 sites listed with rationale) | |
| - Full bibliography: RESEARCH.md (~6 pages) | |
| - **Any number in this presentation can be debated — we welcome it** | |
| ## Slide 8: Try It | |
| - HF Space: [link] | |
| - GitHub: [link] | |
| - Training notebook: [Colab link] | |
| - Blog: [HF post link] | |
| - Video: [YouTube link] | |
| - **Questions?** | |