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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Sep 9·5 min read
DART: Distributional Adversarial Recurrent Training for Algorithm Learning
TL;DR
DART, or Distributional Adversarial Recurrent Training, enhances recurrent reasoning models by using a local target distribution instead of single-point supervision. This approach improves solution quality and stability across various complex tasks like Maze and Chess.
✦ Why It Matters
Implement DART in your RRM training to enhance model robustness and solution quality in complex tasks.
Key Takeaways
- Adopt DART for training RRMs to improve performance on complex tasks.
- Utilize local target distributions to enhance learning signals.
- Combine DART with existing training methods for better stability.
- Evaluate model robustness under distribution shifts using DART.
How It Works
DART replaces single-point supervision with a local target distribution around the ground-truth solution. This approach uses an adversarial objective to align model outputs with the distribution, providing a richer learning signal that encourages stable iterative trajectories toward valid solutions.