Defines an abstract Controller class with platform-specific implementations (AndroidController, DesktopController, WebController) for device interaction (screenshot, tap, type, slide, back, home). Demonstrates clean abstraction for GUI automation, isolating platform details while providing a uniform interface for agent logic. Easily reusable in other automation frameworks.

Standardizes the action space across agent versions (click, input_text, scroll, etc.) and provides robust JSON parsing/validation utilities for LLM outputs. Eliminates ad-hoc action formatting, ensuring consistency between perception and execution layers. The pattern is self-contained with minimal dependencies, making it portable to other agent systems.

Calculates effective sequence lengths for multimodal inputs (text + image) in Qwen-VL models, accounting for vision token encoding. Solves the non-obvious problem of aligning text and vision sequences in transformer models, preventing truncation errors. Encapsulated utility with clear inputs/outputs, reusable across similar multimodal architectures.

Implements a modular reward manager for RLHF training that processes rule-based or model-based rewards. Demonstrates clean separation of reward computation from training loop, supporting various reward functions through a common interface. The abstract base pattern allows easy extension for custom rewards while maintaining training stability.