Summary: NAS with RL

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2025-02-25 2025-04-25 459 words 3 minutes

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The paper introduces Neural Architecture Search (NAS), a method that uses a RNN as a “controller” to automatically generate neural network architectures.
The controller is trained via reinforcement learning, with the validation accuracy of the “child network” serving as the reward signal.
It demonstrates the ability to discover architectures for both convolutional (CIFAR-10) and recurrent networks (Penn Treebank) that rival or surpass human-designed models.

It proposes a flexible controller RNN that can sample variable-length architecture descriptions, expanding beyond fixed hyperparameter settings seen in standard Bayesian or random search methods.
It applies RL to directly optimize architectures for higher validation accuracy, rather than relying on supervised signals or manual heuristics.
It shows state-of-the-art performance on CIFAR-10 and Penn Treebank, suggesting the automatically discovered architectures can compete with the best human-engineered models.

CIFAR-10 image classification:

The authors let the controller search for convolutional networks with optional skip connections and pooling layers.
The discovered architectures were trained and tested, achieving error rates better than leading manually designed networks.

Penn Treebank language modeling:

The controller was used to search for novel recurrent cell architectures (beyond LSTM).
The best discovered cell achieved test perplexities better than previous state-of-the-art models.
The authors also tested the new cell on character-level language modeling (still on PTB) and briefly on a machine translation task (GNMT framework), showing further performance gains.

Training thousands of candidate architectures is extremely expensive, even with large-scale parallel resources.
Although the search space is large, it is still constrained by the design choices (e.g., certain filter sizes, skip-connection types, or cell structures).
While the discovered architectures worked well on CIFAR-10 and PTB, there is no guarantee they are optimal for other datasets or tasks.
Setting up distributed reinforcement learning for large-scale architecture search requires significant engineering effort.

Investigate more efficient or sample-efficient search strategies, such as weight-sharing, early stopping, or more advanced reinforcement learning algorithms.
Incorporate more types of layers or operations (e.g., attention mechanisms, dynamic routing) while keeping the search process tractable.
Explore ways to transfer discovered architectures across tasks, possibly adapting or fine-tuning them with minimal retraining.
Searching architectures under memory, latency, or power constraints to make the method more practical for real-world applications.

CIFAR-10: A benchmark image classification dataset containing 60,000 32×32 color images in 10 classes.
Penn Treebank (PTB): A benchmark dataset for language modeling used to evaluate the performance of RNN.
Weight Sharing: Reusing the same weights across different parts of the network to reduce computational cost during the search process.