However, the inferred reward function often fails to capture the underlying task objective.

Unfortunately, existing faithfulness criteria for PRP are limited to coarse-grained LLM-based scoring without a clear definition or formulation.

Our code is available at: https://github.com/LINs-lab/ReLA .

In real-world applications, event data is typically received in a streaming manner, where the distribution of patterns may shift over time. Additionally, privacy and memory constraints are commonly observed in practical scenarios, further compounding the challenges.

In a single-agent setting, IL was shown to be done efficiently via an inverse soft-Q learning process.