Inverse Reinforcement Learning Meets Large Language Model Post-Training: Basics, Advances, and Opportunities

Summary

This paper talks about how inverse reinforcement learning (IRL) is used to make large language models (LLMs) better aligned with human goals, by teaching them to understand rewards based on human feedback.

What's the problem?

The problem is that getting LLMs to behave in ways that match what humans want—called alignment—is very hard, because it’s difficult to define exactly what the model should aim for and how to measure success.

What's the solution?

The authors review recent progress in using IRL, a method where models learn the reward system by observing examples and preferences from humans, rather than just guessing next words. They discuss how constructing neural reward models and addressing challenges in training and evaluating these systems help make LLMs more controllable and reliable.

Why it matters?

This matters because better alignment using IRL helps create AI that behaves more safely and predictably, improving how useful and trustworthy these language models are for real-world applications.