A method that trains research agents with a world model as a cheap stand-in for real execution, plus anchored bias and noise corrections, reports 3-4x faster training and equal or better performance than real-environment RL.
Tree of thoughts: Deliberate problem solving with large language models
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Scaling Automatic Research Agents via World Models
A method that trains research agents with a world model as a cheap stand-in for real execution, plus anchored bias and noise corrections, reports 3-4x faster training and equal or better performance than real-environment RL.