Parameter Interpolation

Summary: Parameter Interpolation is a method for merging domain-specialized models by combining their parameters through weighted averaging, enabling the creation of multi-domain agents without the computational cost of joint training. This technique allows models trained on different specialized tasks to be combined into a unified agent that maintains capabilities across multiple domains.

Overview

Parameter Interpolation addresses a fundamental challenge in Multi-Domain Agent Training: how to combine specialized models without expensive retraining. Instead of jointly training a model across all domains simultaneously, this approach trains separate models for each specialized domain, then merges their learned parameters through weighted combination.

The method works by taking the parameter weights from multiple domain-specific models and computing a weighted average to create a new model that inherits capabilities from all constituent models. This is particularly valuable for GUI Agents that need to operate across diverse environments like desktop applications, mobile interfaces, web browsers, and games.

In the UI-TARS-2 framework, Parameter Interpolation enables the creation of unified agents by merging models specialized for different GUI environments. Rather than training a single model on all domains from scratch—which would be computationally expensive and potentially lead to interference between domain-specific skills—separate models are trained for each domain and then combined.

Key Details

  • Cost Efficiency: Eliminates the need for expensive joint training across multiple domains, significantly reducing computational requirements
  • Domain Specialization Preservation: Maintains the specialized capabilities learned by individual domain-specific models while creating a unified agent
  • Implementation Method: Uses weighted parameter averaging to combine model weights from different specialized agents
  • Application in UI-TARS-2: Successfully merges GUI agents specialized for different environments (desktop, mobile, browser, games) into a single multi-domain agent
  • Training Strategy: Enables parallel development of domain experts followed by efficient combination, rather than sequential or joint training approaches
  • Performance Maintenance: Allows the combined model to retain strong performance across all constituent domains without catastrophic forgetting

Relationships

  • Multi-Turn Reinforcement Learning — provides the training framework for individual domain-specialized models before interpolation
  • GUI Agents — primary application domain where different interface types require specialized training
  • Data Flywheel — generates domain-specific training data for each specialized model before parameter combination
  • Domain Adaptation — alternative approach that requires additional training, whereas parameter interpolation avoids this cost
  • Model Merging — broader category of techniques for combining multiple models, of which parameter interpolation is a specific method
  • Transfer Learning — related approach but focuses on adapting existing models rather than combining multiple specialized models

Sources