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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....
EMPIRIC, an agent that learns a residual world model: a physics engine extended with code for the missing mechanisms, that learns interpretable, reusable models, and solves more tasks with fewer environment interactions than all three baselines.
The results suggest that one way humans continually grow their knowledge is by mentally representing many hypotheses spanning language-like and program-like representations, then revising those hypotheses to approximate Bayesian updates, while a bottom-up neural mechanism (an LLM) makes inference both tractable and lea...
DiG-bench (Discovery in Games) is released, a new benchmark for discovering new knowledge with experimentation in controlled environments where the objective is unknown, which provides games at seven tiers of difficulty for AI agents.
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