3 papers
cs.LG2026
The Alignment Auditor: A Bayesian Framework for Verifying and Refining LLM Objectives
Matthieu Bou, Nyal Patel, Arjun Jagota +2
The objectives that Large Language Models (LLMs) implicitly optimize remain dangerously opaque, making trustworthy alignment and auditing a grand challenge. While Inverse Reinforce…
cs.LG2026
Learning from Failures: Understanding LLM Alignment through Failure-Aware Inverse RL
Nyal Patel, Matthieu Bou, Arjun Jagota +2
Reinforcement Learning from Human Feedback (RLHF) aligns Large Language Models (LLMs) with human preferences, yet the underlying reward signals they internalize remain hidden, posi…
cs.CL2025
Insights from the Inverse: Reconstructing LLM Training Goals Through Inverse Reinforcement Learning
Jared Joselowitz, Ritam Majumdar, Arjun Jagota +4
Large language models (LLMs) trained with Reinforcement Learning from Human Feedback (RLHF) have demonstrated remarkable capabilities, but their underlying reward functions and dec…