Level Up: Defining and Exploiting Transitional Problems for Curriculum Learning

2024-01-05 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Mathematics & Computational Sciences · Depth: Expert, extended

Summary

The paper introduces a novel method for curriculum learning by defining and exploiting "transitional problems" in machine learning. Unlike static or dynamic difficulty scoring, this approach directly measures problem difficulty relative to a model's ability, identifying problems consistently easier as competence increases. Applied to chess and mathematics, training on a "level-up" curriculum using these transitional problems efficiently improves model competence to the next tier. This method yields interpretable problems and learner-specific curricula, outperforming other training strategies, including i.i.d. sampling. Experiments demonstrate transferability across models (e.g., Qwen2.5-0.5B trained with transitional problems from the Qwen2.5 model family) and datasets (e.g., GSM8k to Orca-200k), showing robustness and improved learning outcomes.

Key takeaway

For Machine Learning Engineers optimizing model training efficiency, this research suggests adopting a "level-up" curriculum based on transitional problems. By identifying problems that mark specific competence boundaries for your model, you can create a learner-specific training sequence that significantly outperforms i.i.d. sampling and other difficulty measures. Consider generating a model series from checkpoints or model families to define these problems, especially for large language models like Qwen2.5, to achieve more efficient progress.

Key insights

Transitional problems define competence boundaries, enabling efficient "level-up" curriculum learning for ML models.

Principles

• Difficulty is model-relative, not static.
• Transitional problems mark competence levels.
• Monotonicity ensures consistent solvability.

Method

Identify a model series of increasing strength. Define transitional problems as those solvable by models at or above a given competence level, with a monotonicity constraint. Construct a "level-up" curriculum using these problems.

In practice

• Apply to chess game positions and puzzles.
• Use for cross-model knowledge transfer (e.g., Qwen2.5 family).
• Transfer curricula across similar datasets (e.g., GSM8k to Orca).

Topics

• Curriculum Learning
• Transitional Problems
• Model Competence
• Training Efficiency
• Chess AI
• Mathematical Reasoning

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.