curriculum learning

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      Curriculum learning is a technique in machine learning in which a model is trained on examples of increasing difficulty, where the definition of "difficulty" may be provided externally or discovered as part of the training process. This is intended to attain good performance more quickly, or to converge to a better local optimum if the global optimum is not found.


      Approach


      Most generally, curriculum learning is the technique of successively increasing the difficulty of examples in the training set that is presented to a model over multiple training iterations. This can produce better results than exposing the model to the full training set immediately under some circumstances; most typically, when the model is able to learn general principles from easier examples, and then gradually incorporate more complex and nuanced information as harder examples are introduced, such as edge cases. This has been shown to work in many domains, most likely as a form of regularization.
      There are several major variations in how the technique is applied:

      A concept of "difficulty" must be defined. This may come from human annotation or an external heuristic; for example in language modeling, shorter sentences might be classified as easier than longer ones. Another approach is to use the performance of another model, with examples accurately predicted by that model being classified as easier (providing a connection to boosting).
      Difficulty can be increased steadily or in distinct epochs, and in a deterministic schedule or according to a probability distribution. This may also be moderated by a requirement for diversity at each stage, in cases where easier examples are likely to be disproportionately similar to each other.
      Applications must also decide the schedule for increasing the difficulty. Simple approaches may use a fixed schedule, such as training on easy examples for half of the available iterations and then all examples for the second half. Other approaches use self-paced learning to increase the difficulty in proportion to the performance of the model on the current set.
      Since curriculum learning only concerns the selection and ordering of training data, it can be combined with many other techniques in machine learning. The success of the method assumes that a model trained for an easier version of the problem can generalize to harder versions, so it can be seen as a form of transfer learning. Some authors also consider curriculum learning to include other forms of progressively increasing complexity, such as increasing the number of model parameters. It is frequently combined with reinforcement learning, such as learning a simplified version of a game first.
      Some domains have shown success with anti-curriculum learning: training on the most difficult examples first. One example is the ACCAN method for speech recognition, which trains on the examples with the lowest signal-to-noise ratio first.


      History


      The term "curriculum learning" was introduced by Yoshua Bengio et al in 2009, with reference to the psychological technique of shaping in animals and structured education for humans: beginning with the simplest concepts and then building on them. The authors also note that the application of this technique in machine learning has its roots in the early study of neural networks such as Jeffrey Elman's 1993 paper Learning and development in neural networks: the importance of starting small. Bengio et al showed good results for problems in image classification, such as identifying geometric shapes with progressively more complex forms, and language modeling, such as training with a gradually expanding vocabulary. They conclude that, for curriculum strategies, "their beneficial effect is most pronounced on the test
      set", suggesting good generalization.
      The technique has since been applied to many other domains:

      Natural language processing:
      Part-of-speech tagging
      Intent detection
      Sentiment analysis
      Machine translation
      Speech recognition
      Image recognition:
      Facial recognition
      Object detection
      Reinforcement learning:
      Game-playing
      Graph learning
      Matrix factorization


      References




      Further reading


      Curriculum Learning: A Survey
      A Survey on Curriculum Learning
      Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey
      Curriculum learning at IEEE Xplore

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    curriculum learning

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    deep rl - What is curriculum learning in reinforcement learning ...

    Apr 29, 2023 · Curriculum learning is a general technique for deep learning, which got recently applied to also deep reinforcement learning. It's about designing tasks to guide the learning process of the network or agent. This can be particularly useful to bootstrap the network for very hard problems, and even to achieve better convergence.

    Can I speed up NN training by manually guiding training?

    Apr 22, 2023 · Such curriculum learning approach has been shown to be beneficial both in deep learning and reinforcement learning problems. Actually there is much interest in automatically building the curriculum : there are some example of this in the literature, allowing to automatically generate a curriculum according to the network's training performance.

    machine learning - How do we call the technique of increasing the ...

    Dec 12, 2024 · Curriculum learning is about simplifying the original task and increasing its difficulty with the goal to make the model learn first a simple task, then a more complicated one. $\endgroup$ – nbro Commented Dec 13, 2024 at 0:18

    How to deal with changing environment in reinforcement learning

    Mar 4, 2022 · This is called curriculum learning and the idea is to present easier training examples to the agent at the beginning of training and steadily increase the difficulty of the environment. In turn, the agent will reach the goal in the easier environments, obtain some reward, and learn.

    What is the reinforcement learning reward function for reasoning …

    Jan 25, 2025 · Similarly in Polu et al 2020 paper "Formal Mathematics Statement Curriculum Learning", proofs here are produced by a language model acting as problem-solving steps or strategies in a similar way, you don't need to worry about possible different DAG proof paths or trees at all. Incidentally they cautioned against applying simple RL for math ...

    deep rl - Reinforcement learning model with games that have …

    Nov 19, 2024 · You might want to use a curriculum learning approach, starting with smaller grids and simpler patterns; Algorithm Suggestions: Given the large action space, Q-learning based approaches might struggle; Consider trying: Monte Carlo Tree Search (MCTS) combined with deep learning (similar to AlphaGo's approach) Hierarchical Reinforcement Learning (HRL)

    machine learning - What are the limitations of this theoretical ...

    Dec 4, 2024 · We show that at same compute budget, expert iteration, by which we mean proof search interleaved with learning, dramatically outperforms proof search only. We also observe that when applied to a collection of formal statements of sufficiently varied difficulty, expert iteration is capable of finding and solving a curriculum of increasingly ...

    machine learning - Neural network for game - Artificial Intelligence ...

    Nov 6, 2023 · Search for e.g. "reinforcement learning connect 4" to get example grid-based games that will be very similar to solve as yours. You don't have to use neural networks or reinforcement learning. For a game as simple as yours, it is possible to write agents purely on game tree search techniques.

    deep learning - How Come My (D)DQN Fails To Learn? - Artificial ...

    Jan 19, 2022 · You can apply curriculum learning to make the environment less ambiguous during early training. The principle of this strategy is to provide easier training examples which do not require many random actions to reach the goal. As the agents learn the complexity of the environment is increased and it can transfer knowledge.

    machine learning - Neural network with a variable # of neurons ...

    Jun 27, 2024 · There are existing learning methods that allow NN architecture to change, but I have not seen any that would change the input layer, since that is usually defined by the problem being solved - so it appears you want to change the data or problem, perhaps as a form of curriculum learning (where you start with a simple problem and use that to ...