Self-Generated In-Context Examples Improve LLM Agents for Sequential Decision-Making Tasks
Vishnu Sarukkai, Zhiqiang Xie, Kayvon Fatahalian
2025-05-02
Summary
This paper talks about how large language model agents can get better at making a series of decisions by creating and learning from their own examples, instead of relying on special instructions from humans.
What's the problem?
AI agents often struggle with tasks that require making several decisions in a row, especially if they don't have lots of examples or detailed guidance made by experts for each specific task.
What's the solution?
The researchers showed that if the AI generates its own practice examples and learns from them, it can improve its performance on different decision-making challenges, even without extra help or custom instructions.
Why it matters?
This matters because it makes AI agents more flexible and capable, allowing them to handle new problems on their own, which is useful for things like planning, robotics, and helping people with complex tasks.
Abstract
LLM agents can improve performance on sequential decision-making tasks by learning from self-generated examples, enhancing test performance across multiple benchmarks without task-specific knowledge engineering.