TAB-PO: Preference Optimization with a Token-Level Adaptive Barrier for Token-Critical Structured Generation
arXiv:2603.00025v2 Announce Type: replace Abstract: Direct Preference Optimization (DPO) is an effective and widely adopted approach for offline alignment but is poorly matched to ontology-driven structured prediction, where preferred and rejected JSON objects often differ in only a few schema-defining tokens. In this low-edit-distance regime, sequence-level DPO spreads gradient mass across non-critical serialization tokens (gradient dilution) and can reduce likelihood on rare, under-confident preferred schema tokens (token erosion). To address these limitations, we first develop a confusion-a
The proliferation of AI systems requiring structured outputs and the limitations of current preference optimization methods necessitate more refined alignment techniques.
Improving the accuracy and efficiency of preference optimization for structured generation directly impacts the reliability and safety of AI agents and applications.
This research introduces a method for better aligning AI models with human preferences in critical structured tasks, overcoming limitations of existing DPO approaches.
- · AI developers
- · AI safety researchers
- · Companies using structured data in AI
- · Inefficient AI alignment methods
- · Generative AI models with poor structured output
More precise and reliable AI systems for various structured generation tasks will become available.
This improved fidelity could accelerate the deployment of AI agents in sensitive domains requiring high accuracy for outputs like code or legal documents.
Enhanced structured prediction capabilities may lead to new forms of automated decision-making and workflow automation previously deemed too risky.
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Read at arXiv cs.CL