PolyAlign: Conditional Human-Distribution Alignment

2026-06-11 · Source: Computation and Language · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Natural Language Processing · Depth: Expert, quick

Summary

PolyAlign is a new distribution-aware alignment framework designed to address the limitation of current post-training methods like supervised fine-tuning (SFT) and preference optimization, which typically align language models toward a single global assistant behavior. This global alignment suppresses the natural variation of human responses across diverse contexts such as languages, tasks, and dialogue settings. PolyAlign tackles this by implementing conditional human-distribution alignment, organizing bilingual interaction data into bucket-specific human reference distributions defined by language, interaction track, response family, and length. The framework integrates Bucket-Aware SFT, which balances optimization across heterogeneous buckets, with Human-Distribution Preference Optimization (HDPO), a method that regularizes preference learning using critic-estimated distance to bucket-specific human support. Evaluated across a bilingual suite covering English and Chinese single- and multi-turn settings, PolyAlign demonstrates improved conditional naturalness and distributional faithfulness while maintaining competitive task utility.

Key takeaway

For Machine Learning Engineers developing language models, if your goal is to achieve more natural and contextually appropriate responses, you should move beyond traditional global alignment objectives. PolyAlign demonstrates that aligning models to interaction-aware human response distributions, rather than a universal style, significantly improves conditional naturalness and distributional faithfulness. Consider implementing bucket-specific data organization and combining Bucket-Aware SFT with Human-Distribution Preference Optimization to enhance your model's contextual adaptability.

Key insights

PolyAlign aligns language models to context-specific human response distributions, overcoming limitations of global alignment for improved naturalness and faithfulness.

Principles

• Align models to context-specific human distributions.
• Balance optimization across heterogeneous data buckets.
• Regularize preference learning with human support distance.

Method

PolyAlign organizes bilingual data into bucket-specific human reference distributions. It combines Bucket-Aware SFT for balanced optimization with Human-Distribution Preference Optimization (HDPO) for preference learning regularization.

In practice

• Use bucket-specific human reference distributions.
• Apply Bucket-Aware SFT for diverse datasets.
• Implement HDPO for preference learning regularization.

Topics

• Language Model Alignment
• Supervised Fine-tuning
• Preference Optimization
• Human-Distribution Alignment
• Bilingual NLP
• Conditional Generation

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, NLP Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computation and Language.