Objective-Conditioned Active Preference Learning for Multi-Objective Alignment

arXiv:2611.05506v1 Announce Type: new Abstract: Aligning large language models (LLMs) with several objectives, such as helpfulness, harmlessness, and honesty, typically relies on preference judgment for each objective, growing the annotation cost linearly with the number of objectives. Active preference learning can reduce this cost by selecting the comparisons whose labels are expected to change the policy most. Existing remedies either lack an objective-aware query selection mechanism for the multi-objective setting or regard conflicting preferences as harmful, therefore excluding them from the training data. We introduce OGC-DPO (Objective-conditioned Gradient Coverage DPO), an active learning framework that treats each (response pair, objective) combination as a separate query and measures its informativeness through the policy's own implicit gradient, without relying on an external reward model. OGC-DPO then selects the queries that most expand each objective's gradient coverage. Empirical experiments across varying numbers of objectives demonstrate the effectiveness of OGC-DPO over existing active preference learning methods under the same annotation cost. Further analysis demonstrates that informative conflicts, rather than being uniformly harmful, provide a useful policy training signal.

Reduces annotation cost for aligning LLMs with multiple objectives, potentially increasing their adoption and impact.

This method improves alignment of language models with multiple objectives efficiently, potentially enhancing AI's helpfulness and safety. However, it also involves managing conflicting preferences, which could complicate alignment and have uncertain societal effects. Evidence is mainly technical and experimental within academic scope, limiting direct societal impact claims.

This is an incremental technical improvement to LLM alignment efficiency, reducing annotation cost for multi-objective preference tuning. It has modest positive implications for alignment quality and cost but no broad societal claim is made. Impact is narrow and methodological, not transformative.

This paper presents a technical improvement to methods for aligning large language models with multiple objectives. The innovation aims to make the alignment process more efficient by reducing annotation costs. It primarily concerns a technical optimization in AI development rather than direct societal impact, therefore it is genuinely neutral.

The paper presents a method to reduce the cost of aligning LLMs with multiple objectives. It shows that conflicts in preferences can be useful. This implies more efficient and effective AI development.

SOURCE     arXiv cs.LG (Machine Learning) (tier 1)
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DOCUMENT   d2795f0c-26de-4fb8-9440-ae8cd05d0773
           https://arxiv.org/abs/2611.05506
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EVIDENCE   2 extracted excerpts
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MODEL RUN  5 runs, methodology 1.0.1
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SCORE      +18  (Mixed or uncertain)
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CONFIDENCE 63%