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[2602.15338] Discovering Implicit Large Language Model Alignment Objectives
Summary
This article presents a framework called Obj-Disco, which identifies implicit alignment objectives in large language models (LLMs) to enhance transparency and safety in AI development.
Why It Matters
Understanding the alignment objectives of LLMs is crucial for mitigating risks associated with misalignment and reward hacking. The Obj-Disco framework offers a novel approach to uncovering these objectives, paving the way for safer AI systems and more effective alignment strategies.
Key Takeaways
- Obj-Disco decomposes alignment reward signals into interpretable objectives.
- The framework captures over 90% of reward behavior across various tasks.
- It identifies latent misaligned incentives that can emerge during model training.
- The approach enhances transparency in AI alignment processes.
- Robust evaluations demonstrate the framework's effectiveness across model sizes and alignment algorithms.
Computer Science > Machine Learning arXiv:2602.15338 (cs) [Submitted on 17 Feb 2026] Title:Discovering Implicit Large Language Model Alignment Objectives Authors:Edward Chen, Sanmi Koyejo, Carlos Guestrin View a PDF of the paper titled Discovering Implicit Large Language Model Alignment Objectives, by Edward Chen and Sanmi Koyejo and Carlos Guestrin View PDF HTML (experimental) Abstract:Large language model (LLM) alignment relies on complex reward signals that often obscure the specific behaviors being incentivized, creating critical risks of misalignment and reward hacking. Existing interpretation methods typically rely on pre-defined rubrics, risking the omission of "unknown unknowns", or fail to identify objectives that comprehensively cover and are causal to the model behavior. To address these limitations, we introduce Obj-Disco, a framework that automatically decomposes an alignment reward signal into a sparse, weighted combination of human-interpretable natural language objectives. Our approach utilizes an iterative greedy algorithm to analyze behavioral changes across training checkpoints, identifying and validating candidate objectives that best explain the residual reward signal. Extensive evaluations across diverse tasks, model sizes, and alignment algorithms demonstrate the framework's robustness. Experiments with popular open-source reward models show that the framework consistently captures > 90% of reward behavior, a finding further corroborated by human eva...
