A 300-page Princeton thesis maps RL’s shift from games to world models
udmrzn · x · 2026-07-29
A Princeton PhD thesis by Zihan Ding ties together two major RL threads over more than 300 pages.
- Part 1: games and multi-agent RL — learning near-Nash strategies, robustness against exploitation, and extensions to fighting games, multi-player games, and network load balancing.
- Part 2: foundation-model-era RL — diffusion world models, generative policy learning, efficient video generation, interactive video world models, and memory mechanisms for long-horizon prediction.
The core message is that RL is shifting from “learn to act in a fixed environment” to “use generative models to understand and predict the world, then plan with that model.”
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