Automated Curriculum Learning for Neural Networks

Graves et al., 2017


  • Maximize learning efficiency by following a curriculum
  • Measure of the amount that the network learns from each data sample used as reward
  • Nonstationary multi-armed bandit algorithm
  • Consider a variety of signals based on rate of increase in prediction accuracy and network complexity
  • Experimental results with LSTMs on three curricula
  • Links: [ website ] [ pdf ]


  • Based on the idea that a curriculum of tasks of increasing difficulty can accelerate learning
  • Curriculum learning is highly sensitive to the exact progression through tasks (e.g. when to switch to next task, return to old tasks, what is the measure of difficulty etc.)


  • Treat picking next task as stochastic policy, optimized for maximizing learning progress

  • Progress signal is evaluated for each training example

  • Nonstationary multi-armed bandit

  • task is a distribution D over batches from $ X := (A \times B)^N $, where $ A $ and $ B $ are the set of inputs and outputs, respectively

  • curriculum is an esemble of task $ D_1, …, D_N $

  • syllabus is a time-varying sequence of distributions over tasks

  • View curriculum containing N tasks as an N-armed bandit (Exp3 algorithm Auer et al. 2002)

  • Adaptively rescale rewards to [-1,1] based on highest and lowest quantiles using reservoir sampling

  • Learning progress signals

    • Policy should maximize rate at which model minimizes the loss, should be reflected in reward
  • Loss-driven progress

    • Prediction gain (PG) - change in loss for a sample, before and after training on it
    • Gradient prediction gain (GPG) - first-order approximation to prediction gain. Avoids addition forward pass of PG
    • Self prediction gain (SPG) - change in loss for a new sample from the same task
    • Target prediction gain (TPG) - change in loss for a new sample from the target task
    • Mean prediction gain (MPG) - change in loss for a new sample from all tasks
  • Complexity progress signals

    • Based on Minimum Description Length principle - tradeoff between model complexity and data compression
    • Variational complexity gain (VCG) - change in model complexity induced by sample
    • Gradient variational complexity gain (GVCG) - first order approximation to VCG
    • L2 gain (L2G) - change in l2 norm of model parameters
  • Bias-variance tradeoff for different progress signals.


  • Synthetic N-gram language modelling

    • character-level n-gram model on Bible data
    • n from 0 to 10
    • For each model, generate dataset of 1M characters, divided into 150 characters disjoint sequences.
    • LSTM trained to predict last 100 characters from first 50
    • Intuitively, higher n produces more structure and should have higher learning progress
    • Complexity progress signals quickly result in a strong focus on the 10-gram task
    • Except VCG
    • Loss based signals trend towards higher n, but are slower
    • Less so in PG, GPG
  • Repeat Copy

    • Network is trained to repeat a random sequence a given number of times
    • Difficulty based on length of sequence and number of repeats, both vary from 1 to 13 for this experiment (169 tasks in total)
    • ML training
    • PG, SPG, TPG slightly faster than uniform
    • L2G, GL2G, GPG slower
    • VI training
    • GVCG faster than uniform
    • VCG slower
    • However, training on target task failed to learn, indicating need for curriculum
    • One example showed GVCG syllabus first focus on short sequences with high repeats, then long sequences with low repeats.
    • Loss is reduced in many tasks that the policy does not focus on
  • bAbI

    • 20 synthetic question-answering problems
    • Created larger dataset of 1M sotires for each of the 20 tasks
    • PG, SPG better than uniform, others worse
    • VI training worse than uniform


  • Best learning progress signal depends on the task
  • Some may perform worse than uniform policy
  • PG for ML training and GVCG for VI training generally most consistent
  • Both only rely on current sample
  • Curriculum composed of a small number of very similar tasks
Elias Z. Wang
Elias Z. Wang
AI Researcher | PhD Candidate