Inverse Reinforcement Learning Meets Large Language Model Post-Training:
  Basics, Advances, and Opportunities
In the era of Large Language Models (LLMs), alignment has emerged as a
fundamental yet challenging problem in the pursuit of more reliable,
controllable, and capable machine intelligence. The recent success of reasoning
models and conversational AI systems has underscored the critical role of
reinforcement learning (RL) in enhancing these systems, driving increased
research interest at the intersection of RL and LLM alignment. This paper
provides a comprehensive review of recent advances in LLM alignment through the
lens of inverse reinforcement learning (IRL), emphasizing the distinctions
between RL techniques employed in LLM alignment and those in conventional RL
tasks. In particular, we highlight the necessity of constructing neural reward
models from human data and discuss the formal and practical implications of
this paradigm shift. We begin by introducing fundamental concepts in RL to
provide a foundation for readers unfamiliar with the field. We then examine
recent advances in this research agenda, discussing key challenges and
opportunities in conducting IRL for LLM alignment. Beyond methodological
considerations, we explore practical aspects, including datasets, benchmarks,
evaluation metrics, infrastructure, and computationally efficient training and
inference techniques. Finally, we draw insights from the literature on
sparse-reward RL to identify open questions and potential research directions.
By synthesizing findings from diverse studies, we aim to provide a structured
and critical overview of the field, highlight unresolved challenges, and
outline promising future directions for improving LLM alignment through RL and
IRL techniques.
687e2995d5ffc3b4b0fa7751
Hao Sun
admin_assigned
2025-07-21T16:04:12.174Z
64de3b7b293d686360302549
/avatars/b68af8fa86614796c6e8036379dee228.svg
Hao Sun
user
Holarissun
687e2995d5ffc3b4b0fa7752
Mihaela van der Schaar
Inverse Reinforcement Learning Meets Large Language Model Post-Training:
  Basics, Advances, and Opportunities
In the era of Large Language Models (LLMs), alignment has emerged as a
fundamental yet challenging problem in the pursuit of more reliable,
controllable, and capable machine intelligence. The recent success of reasoning
models and conversational AI systems has underscored the critical role of
reinforcement learning (RL) in enhancing these systems, driving increased
research interest at the intersection of RL and LLM alignment. This paper
provides a comprehensive review of recent advances in LLM alignment through the
lens of inverse reinforcement learning (IRL), emphasizing the distinctions
between RL techniques employed in LLM alignment and those in conventional RL
tasks. In particular, we highlight the necessity of constructing neural reward
models from human data and discuss the formal and practical implications of
this paradigm shift. We begin by introducing fundamental concepts in RL to
provide a foundation for readers unfamiliar with the field. We then examine
recent advances in this research agenda, discussing key challenges and
opportunities in conducting IRL for LLM alignment. Beyond methodological
considerations, we explore practical aspects, including datasets, benchmarks,
evaluation metrics, infrastructure, and computationally efficient training and
inference techniques. Finally, we draw insights from the literature on
sparse-reward RL to identify open questions and potential research directions.
By synthesizing findings from diverse studies, we aim to provide a structured
and critical overview of the field, highlight unresolved challenges, and
outline promising future directions for improving LLM alignment through RL and
IRL techniques.
A review of recent advances in aligning Large Language Models using inverse reinforcement learning, emphasizing the construction of neural reward models and addressing challenges in training and evaluation.
Large Language Models alignment reinforcement learning inverse reinforcement learning neural reward models sparse-reward RL