ATLAS uses active learning with disentangled RNN ensembles to design experiments that recover RL agent models from bandit behavior 5-10x more efficiently than random or expert baselines in simulations.
Reinforcement learning in the brain.Journal of Mathematical Psychology, 53(3):139– 154, 2009
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LLMs exhibit Bayesian-like hypothesis updating with strong-sampling bias and an evaluation-generation gap but generalize poorly outside observed data.
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ATLAS: Active Theory Learning for Automated Science
ATLAS uses active learning with disentangled RNN ensembles to design experiments that recover RL agent models from bandit behavior 5-10x more efficiently than random or expert baselines in simulations.
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Hypothesis generation and updating in large language models
LLMs exhibit Bayesian-like hypothesis updating with strong-sampling bias and an evaluation-generation gap but generalize poorly outside observed data.