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arXiv · 2511.12155

Rethinking Deep Alignment Through The Lens Of Incomplete Learning

Thong Bach·Dung Nguyen·Thao Minh Le·Truyen Tran
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Abstract

Large language models exhibit systematic vulnerabilities to adversarial attacks despite extensive safety alignment. We provide a mechanistic analysis revealing that position-dependent gradient weakening during autoregressive training creates signal decay, leading to incomplete safety learning where safety training fails to transform model preferences in later response regions fully. We introduce base-favored tokens -- vocabulary elements where base models assign higher probability than aligned models -- as computational indicators of incomplete safety learning and develop a targeted completion method that addresses undertrained regions through adaptive penalties and hybrid teacher distillation. Experimental evaluation across Llama and Qwen model families demonstrates dramatic improvements in adversarial robustness, with 48--98% reductions in attack success rates while preserving general capabilities. These results establish both a mechanistic understanding and practical solutions for fundamental limitations in safety alignment methodologies.

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Thong Bach, Dung Nguyen, Thao Minh Le, Truyen Tran. 2025-11-15. Rethinking Deep Alignment Through The Lens Of Incomplete Learning. https://arxiv.org/abs/2511.12155

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