Pinductor leverages language-model priors to learn POMDP world models from limited trajectories, matching privileged-access methods in performance and exceeding tabular baselines in sample efficiency.
Human-level reinforcement learning through theory-based modeling, exploration, and planning
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VLMs and LAMs outperform RL baselines in voxel-wise brain encoding during gameplay, with LAMs showing prompt-asymmetric organization (27% unique action vs -5% unique reasoning) strongest in frontal-motor cortex.
Frontier LRMs match human game-learning behavior and predict fMRI signals an order of magnitude better than RL or Bayesian agents because of their in-context game-state representations.
citing papers explorer
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Learning POMDP World Models from Observations with Language-Model Priors
Pinductor leverages language-model priors to learn POMDP world models from limited trajectories, matching privileged-access methods in performance and exceeding tabular baselines in sample efficiency.
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Brain alignment of reasoning and action representations from vision-language and action models during naturalistic gameplay
VLMs and LAMs outperform RL baselines in voxel-wise brain encoding during gameplay, with LAMs showing prompt-asymmetric organization (27% unique action vs -5% unique reasoning) strongest in frontal-motor cortex.
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Reason to Play: Behavioral and Brain Alignment Between Frontier LRMs and Human Game Learners
Frontier LRMs match human game-learning behavior and predict fMRI signals an order of magnitude better than RL or Bayesian agents because of their in-context game-state representations.