Author

Samuel Gershman

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#artificial intelligence Preprint Sep 2026

Code to Control: Synthesizing Parameterized Reactive Controllers

Recent LLM-based approaches to control either invoke a language model to select actions or synthesize world models that require planning at every decision, introducing latency that can limit real-time use. We introduce Code to Control, an approach that synthesizes Python controllers which execute directly as policies....

Zergham Ahmed, Joshua B. Tenenbaum, Christopher J. Bates et al. · 0 citations

#machine learning Preprint Sep 2026

PlayTrain: An Efficient Reinforcement Learning Framework for LLM-Generated Adaptable JavaScript Games

Through PlayTrain, an RL framework that combines the abilities of large language models to robustly generate JavaScript games from a minimal human prompt, and an efficient pipeline that can run any JS game in a standard'gym'environment, this work reimagine RL VGE development.

R. Truong, Lance Ying, Samuel Gershman et al. · 0 citations