Progressive learning is the training strategy that organizes tasks from simple to complex. It mirrors how humans learn: you master fundamentals before tackling advanced problems. For smaller models, this approach is especially powerful because it maximizes limited capacity.
The Idea of a Curriculum
A curriculum is a sequence that starts with basic concepts and gradually introduces complexity. In AI training, this could mean:
- Single-step arithmetic before multi-step word problems
- Simple code snippets before full program synthesis
- Basic logic puzzles before composite reasoning tasks
This staged approach ensures the model can build on earlier knowledge rather than being overwhelmed.
Why It Works for Small Models
Smaller models have less capacity to store and generalize from raw data. When the training process is structured, the model can form stable representations of foundational concepts. Once those foundations are strong, the model can handle more complex tasks.
In contrast, a random mix of easy and hard problems can confuse learning. The model may fail to recognize the underlying structure, leading to shallow imitation rather than deep reasoning.
Designing the Stages
A progressive curriculum should be intentional:
1) Foundations: basic rules, definitions, and simple examples 2) Compositions: multi-step problems combining earlier rules 3) Variants: problems with altered contexts or constraints 4) Open-ended tasks: reasoning under ambiguity or incomplete information
Each stage should reinforce earlier skills while adding new complexity.
Integrating Reasoning Traces
Reasoning traces play a key role in progressive learning. Early stages might include very detailed traces, while later stages might require the model to infer missing steps. This teaches the model to generalize while still grounding it in explicit reasoning.
Avoiding Curriculum Collapse
There is a risk that a curriculum becomes too narrow, teaching a model to excel in a specific style rather than in general reasoning. To avoid this, curricula should include diverse problem types, multiple reasoning styles, and cross-domain transfer tasks.
You can think of it as teaching the model to reason, not to memorize a single “recipe.”
Evaluation Aligned with Curriculum
Progressive learning also demands aligned evaluation. Each stage should have its own tests to ensure mastery before moving on. Skipping evaluation can hide weaknesses that emerge later in complex tasks.
A good curriculum makes progress measurable. That is critical if the goal is to close the gap between small models and larger systems.