Journey Learning vs Shortcut Learning

Many current machine learning and large language model approaches can be described as “shortcut learning.” This method focuses on achieving quick results by heavily relying on large amounts of data to improve performance. However, it often struggles with generalization, meaning it performs poorly in situations outside its training data, and lacks the ability to self-correct mistakes. While it has driven advancements, shortcut learning shows limitations in handling complex, dynamic, and open-ended challenges, making it less effective for developing truly intelligent AI.

A paradigm shift from “shortcut learning” to “journey learning”. A searching tree for reasoning tasks. For the math problem-solving task, the root node represents the initial problem, while the leaf nodes are final conclusions. Green nodes indicate correct answers, and red nodes incorrect ones. Traditionally, learning focused on supervised training of a direct root-to-leaf shortcut path. This work, however, explores supervised learning of the entire exploration path, encompassing trial-and-error and correction processes.

To address these limitations, we propose a new approach: “journey learning.” This paradigm goes beyond the traditional focus on specific tasks and emphasizes continuous progress through learning, reflection, and adaptation. AI systems that follow this method can evolve over time, improving their ability to handle real-world complexities. Unlike shortcut learning, journey learning equips AI with the capacity to adapt, backtrack, and refine its understanding, aiming to create more human-like intelligence.

Characteristic	Shortcut Learning	Journey Learning
Learning Depth	Surface features and simple correlations	Deep causal relationships and underlying principles
Reasoning Ability	Limited, struggles with complex reasoning	Powerful, demonstrates human-like reasoning
Self-Improvement	Lacks self-correction mechanisms	Continuous self-assessment and improvement
Generalization	Limited, easily affected by data distribution changes	Strong, can handle new situations
Innovation Capacity	Limited, struggles to solve new problems	High, can generate innovative solutions
Data Dependency	Highly dependent on large training datasets	More focused on quality and learning strategies
Interpretability	Poor, often seen as a "black box"	Better, can track internal reasoning processes
Ethical Considerations	May unintentionally amplify data biases	Easier to implement ethical constraints and adjustments
Security	Vulnerable to adversarial attacks	More robust, able to identify potential threats
Long-term Value	Quick results in specific tasks	Paves the way for AGI development
Human Analogy	Exam-oriented education, crash courses	Comprehensive education, lifelong learning

Comparison between Shortcut Learning and Journey Learning.

This shift to journey learning opens up new possibilities in AI research, enabling the creation of systems that can not only perform tasks but also reason and grow, making them more capable of engaging with humans across various domains.