arXiv:2510.06096v3 Announce Type: replace Abstract: The objectives that Large Language Models (LLMs) implicitly optimize remain dangerously opaque, making trustworthy alignment and auditing a grand challenge. While Inverse Reinforcement Learning (IRL) can infer reward functions from behaviour, existing approaches either produce a single, overconfident reward estimate or fail to address the fundamental ambiguity of the task (non-identifiability). This paper introduces a principled auditing framework that re-frames reward inference from a simple estimation task to a comprehensive process for ver
The rapid advancement and deployment of Large Language Models necessitate robust auditing frameworks to ensure their alignment with human objectives and prevent unintended consequences.
Establishing trustworthy methods for verifying and refining LLM objectives is critical for the safe and ethical development of increasingly autonomous AI systems, impacting their societal integration and regulatory landscape.
This framework offers a more principled and comprehensive approach to understanding and rectifying opaque LLM objectives, moving beyond simple estimation to address fundamental ambiguity.
- · AI safety researchers
- · Regulatory bodies
- · Enterprises deploying LLMs
- · Developers of interpretable AI
- · Developers of black-box AI
- · Actors seeking to manipulate LLMs
Increased trust and adoption of AI systems due to improved alignment and auditability.
Development of new compliance and certification standards for AI models based on verifiable objective functions.
Accelerated progress towards more agentic AI systems that can safely operate with defined and auditable goals.
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Read at arXiv cs.LG