arXiv:2502.00225v4 Announce Type: replace Abstract: We evaluate the ability of the current generation of large language models (LLMs) to help a decision-making agent facing an exploration-exploitation tradeoff. While previous work has largely study the ability of LLMs to solve combined exploration-exploitation tasks, we take a more systematic approach and use LLMs to explore and exploit in silos in various (contextual) bandit tasks. We find that reasoning models show the most promise for solving exploitation tasks, although they are still too expensive or too slow to be used in many practical
The rapid advancement and widespread deployment of large language models are creating an urgent need to understand their capabilities and limitations in complex decision-making scenarios.
Understanding how LLMs perform in exploration-exploitation tradeoffs deeply impacts their utility in autonomous systems and agents, determining where human oversight remains essential.
The research systematically separates LLMs' abilities to explore new options versus exploit known good ones, providing a more nuanced view of their cognitive functions in decision-making.
- · AI researchers
- · LLM developers
- · Decision support systems
- · Systems relying on naive LLM integration for complex tasks
- · Heuristics-based decision-making in some contexts
Further research into optimizing LLMs for specific exploration or exploitation requirements will accelerate.
Enterprises will begin to strategically deploy LLMs in roles requiring either exploration or exploitation, rather than generalized decision-making.
The development of hybrid human-AI decision systems will accelerate, leveraging human exploration and LLM exploitation or vice versa, based on task demands.
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