arXiv:2602.15338v2 Announce Type: replace 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, weighte
The increasing complexity and opacity of large language model (LLM) alignment objectives necessitate novel methods for interpretability to mitigate risks before broad deployment.
Understanding and controlling LLM behavior is critical for their safe and effective integration into sensitive applications, directly impacting AI safety and governance discussions.
The ability to automatically decompose complex alignment reward signals into specific, causal objectives could fundamentally change how LLMs are audited, developed, and regulated.
- · AI safety researchers
- · LLM developers
- · Regulatory bodies
- · Organizations deploying LLMs
- · Opaque AI systems
- · Malicious actors exploiting reward hacking
Improved interpretability of LLM alignment objectives will reduce 'unknown unknowns' and enhance model reliability.
This improved understanding could accelerate the development of more robust and ethical AI systems, influencing AI adoption rates.
Standardized objective decomposition frameworks might become a regulatory requirement for AI systems, impacting compliance costs and market entry barriers for new models.
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Read at arXiv cs.LG