Why Steering Works: Toward a Unified View of Language Model Parameter Dynamics
Methods for controlling large language models (LLMs), including local weight fine-tuning, LoRA-based adaptation, and activation-based interventions, are often studied in isolation, obscuring their connections and making comparison difficult. In this work, we present a unified view that frames these interventions as dynamic weight updates induced by a control signal, placing them within a single conceptual framework. Building on this view, we propose a unified preference-utility analysis that separates control effects into preference, defined as the tendency toward a target concept, and utility, defined as coherent and task-valid generation, and measures both on a shared log-odds scale using polarity-paired contrastive examples. Across methods, we observe a consistent trade-off between preference and utility: stronger control increases preference while predictably reducing utility. We further explain this behavior through an activation manifold perspective, in which control shifts representations along target-concept directions to enhance preference, while utility declines primarily when interventions push representations off the model's valid-generation manifold. Finally, we introduce a new steering approach SPLIT guided by this analysis that improves preference while better preserving utility. Code is available at https://github.com/zjunlp/EasyEdit/blob/main/examples/SPLIT.md.
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Ziwen Xu
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2026-04-02T08:18:32.704Z
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Xu Ziwen
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xzwnlp
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Chenyan Wu
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Hengyu Sun
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Haiwen Hong
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Mengru Wang
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Yunzhi Yao
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2026-02-04T12:31:39.850Z
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Yunzhi Yao
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cowTodd
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Longtao Huang
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Hui Xue
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Shumin Deng
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Zhixuan Chu
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Huajun Chen
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Ningyu Zhang
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2026-02-03T13:58:11.354Z
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Ningyu Zhang
Ningyu
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Ningyu
Why Steering Works: Toward a Unified View of Language Model Parameter Dynamics
Methods for controlling large language models (LLMs), including local weight fine-tuning, LoRA-based adaptation, and activation-based interventions, are often studied in isolation, obscuring their connections and making comparison difficult. In this work, we present a unified view that frames these interventions as dynamic weight updates induced by a control signal, placing them within a single conceptual framework. Building on this view, we propose a unified preference-utility analysis that separates control effects into preference, defined as the tendency toward a target concept, and utility, defined as coherent and task-valid generation, and measures both on a shared log-odds scale using polarity-paired contrastive examples. Across methods, we observe a consistent trade-off between preference and utility: stronger control increases preference while predictably reducing utility. We further explain this behavior through an activation manifold perspective, in which control shifts representations along target-concept directions to enhance preference, while utility declines primarily when interventions push representations off the model's valid-generation manifold. Finally, we introduce a new steering approach SPLIT guided by this analysis that improves preference while better preserving utility. Code is available at https://github.com/zjunlp/EasyEdit/blob/main/examples/SPLIT.md.
Large language model control methods are unified under a dynamic weight update framework, revealing a preference-utility trade-off and enabling improved steering through SPLIT approach.
local weight fine-tuning LoRA-based adaptation activation-based interventions dynamic weight updates preference-utility analysis control signal polarity-paired contrastive examples activation manifold valid-generation manifold SPLIT