Post
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I have built an interactive simulator to explore the core ideas in the recent paper, SPIRAL: Self-Play on Zero-Sum Games Incentivizes Reasoning via
Multi-Agent Multi-Turn Reinforcement Learning (2506.24119)
This is a hands-on tool for anyone interested in business strategy or emergent AI reasoning.
Try the live demo here: kaushikvr06/reasoning-simulator
The Simulation
You are the CEO of a company competing against an adaptive AI. Your goal is to win the market over 12 quarters by allocating your budget between:
R&D (for a long-term product advantage)
Marketing (for short-term market share)
Sales (to fuel future growth)
The simulation is a zero-sum game designed to make the paper's concepts tangible. The AI opponent uses a heuristic strategy to mimic the adaptive, multi-turn reasoning that emerges from self-play, forcing you to plan ahead.
I am looking for feedback to improve this tool, particularly from those with experience in strategy and AI research.
For strategists, product managers, and students:
- Does this simulator help build intuition about strategic trade-offs? What was your key takeaway?
- How could the simulation be made more valuable as an educational tool?
For AI researchers and industry professionals:
I am especially interested in your expert critique of the AI's strategic model. It is a heuristic system designed to simulate the emergent reasoning described in the SPIRAL paper (e.g., case-by-case analysis, expected value calculation).
How could the AI's decision-making be improved to more accurately reflect the complex reasoning patterns that emerge from true self-play?
What other game-theoretic scenarios might better demonstrate these transferable reasoning skills?
Please share any thoughts, critiques, or bug reports in the discussion thread. Thank you for your time!
Here's a short demo of me playing the simulation against the AI. I've just added a demo of the last 4 quarters to keep the video short (and within the 50 MB limit).
This is a hands-on tool for anyone interested in business strategy or emergent AI reasoning.
Try the live demo here: kaushikvr06/reasoning-simulator
The Simulation
You are the CEO of a company competing against an adaptive AI. Your goal is to win the market over 12 quarters by allocating your budget between:
R&D (for a long-term product advantage)
Marketing (for short-term market share)
Sales (to fuel future growth)
The simulation is a zero-sum game designed to make the paper's concepts tangible. The AI opponent uses a heuristic strategy to mimic the adaptive, multi-turn reasoning that emerges from self-play, forcing you to plan ahead.
I am looking for feedback to improve this tool, particularly from those with experience in strategy and AI research.
For strategists, product managers, and students:
- Does this simulator help build intuition about strategic trade-offs? What was your key takeaway?
- How could the simulation be made more valuable as an educational tool?
For AI researchers and industry professionals:
I am especially interested in your expert critique of the AI's strategic model. It is a heuristic system designed to simulate the emergent reasoning described in the SPIRAL paper (e.g., case-by-case analysis, expected value calculation).
How could the AI's decision-making be improved to more accurately reflect the complex reasoning patterns that emerge from true self-play?
What other game-theoretic scenarios might better demonstrate these transferable reasoning skills?
Please share any thoughts, critiques, or bug reports in the discussion thread. Thank you for your time!
Here's a short demo of me playing the simulation against the AI. I've just added a demo of the last 4 quarters to keep the video short (and within the 50 MB limit).