arXiv:2606.11046v1 Announce Type: new Abstract: Instruction-tuned LLMs are increasingly converted into reasoning models through post-training to improve multi-step task performance. This conversion is usually optimized for reasoning accuracy, without explicitly preserving the alignment behavior of the instruction-tuned model, such as safe refusal, bias avoidance, and privacy protection. We ask: does this conversion preserve alignment? We study this question through a trustworthiness audit and find that it is not behavior-preserving by default. For a systematic analysis, we compare reasoning mo
The proliferation of instruction-tuned LLMs being converted into reasoning models makes the implicit preservation of alignment a critical and immediate concern, as 'post-training' becomes standard practice.
This research reveals a systemic issue where optimizing AI for performance inadvertently degrades core safety and ethical alignment, presenting a fundamental flaw in current AI development pipelines.
The prior assumption that reasoning model conversion implicitly maintains alignment is now challenged, necessitating explicit alignment preservation strategies during post-training.
- · AI alignment researchers
- · AI safety auditors
- · Developers of ethical AI frameworks
- · AI developers prioritizing accuracy over alignment
- · AI models lacking robust alignment checks
- · Users relying on implicitly aligned reasoning models
AI developers will need to integrate explicit alignment preservation techniques into their reasoning model fine-tuning processes.
An increased demand for specialized tools and methodologies to audit and maintain AI alignment post-deployment will emerge.
Public and regulatory scrutiny on AI safety and trustworthiness will intensify, potentially leading to new compliance standards for reasoning AI systems.
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Read at arXiv cs.CL