Exploration and Exploitation Errors Are Measurable for Language Model Agents

The paper introduces a policy‑agnostic metric that distinguishes exploration and exploitation errors in language model agents operating on partially observable 2D grid maps with unknown symbolic task DAGs. By attributing errors to either exploration or exploitation based on the current state’s potential for productive action, the authors evaluate 35 LM agents and find that both types of errors strongly correlate with task success (R² ≈ 0.88). The metric also uncovers differing exploration/exploitation strategies among agents with similar success rates and demonstrates its applicability to a Minecraft‑style video game.

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• llms
• agents
• benchmarks