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Researchers develop measurable metrics to evaluate how well language model agents balance exploration and exploitation in decision-making tasks.
arXiv cs.AI · April 16, 2026
AI Summary
- •New framework uses controllable 2D grid environments with unknown task DAGs to test language model agents on exploration-exploitation tradeoffs
- •Metric enables policy-agnostic evaluation of agent behavior without requiring access to internal policy mechanisms
- •Environments can be programmatically adjusted to emphasize either exploration or exploitation difficulty, mimicking real embodied AI scenarios
- •Testing reveals that even state-of-the-art language model agents struggle with effectively balancing exploration and exploitation
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