TL;DR: AgentGarten is a virtual-world framework that keeps simulator-grade faithfulness while matching video-model realism, and it taught agents hide-and-seek tool-use strategies in a handful of rounds that took conventional RL millions of episodes. Title: AgentGarten: Code Worlds for Evolving Agents URL: Points 🎮 Couples a scene program (state, rules) with a neural renderer (visuals) through structured conditions like depth and normal maps, decoupling physics from appearance 🔬 A new technique, "Adversarial Forcing," makes history prefilling differentiable via bitwise-exact replay with block-by-block SDPA attention, where standard approaches weren't differentiable at all ⚡ Achieves real-time rendering at 480×832 and 36.5fps on a single H100 🤖 A "playbook" mechanism lets agents carry round-by-round reflections forward, accumulating knowledge with no gradient updates 🏃 In hide-and-seek, shelter-building emerged by round 4 (vs. ~25M episodes for self-play RL) and ramp use by round 10 (vs. ~100M episodes) 📈 Other environments — herding, a quarry loader — also show clear gains within just 4 rounds What's striking is how much learning efficiency you gain just by architecturally separating state dynamics from visual generation. #WorldModels# #AgentLearning#