Pitch: train a generative game model on RL agents playing millions of times, ship the weights
theteknosaur · x · 2026-09-15
A speculative pitch: Rockstar could spend the decade after GTA 6 letting RL agents play its next game millions of times, then train a generative model on that gameplay and sell the weights — players download them and playing is inference.
- The studio still builds worlds, physics, and mechanics; agents explore them, and their sessions become training data for a model that learns to run the game world
- Each player could get unique, novel, much wider gameplay experiences never explicitly designed
- Core idea: build a good-enough game sim to "explain" the game to a neural network
- If neural rendering (DLSS 5) keeps improving, studios could start with simpler visuals and spend more budget on RL training
More from Research
- Subnormal floats are expensive — but only on Intel, benchmarks show — lemire · 2026-09-15
- Adaption AI launches Invent API: training datasets from a prompt, no strings attached — sarahookr · 2026-09-15
- PolyU survey unifies fragmented human-centric AI research with a six-layer progressive framework — jiqizhixin · 2026-09-15
- New arXiv paper: k-robust coalitional alignment for safely delegating review to misaligned agents — Aaroth · 2026-09-15
- Coalitional alignment: a weaker condition that still guarantees multi-agent safety — Aaroth · 2026-09-15
- Coalitional alignment lifts to MDPs: every Nash equilibrium stays safe for the principal — Aaroth · 2026-09-15