DART: Distributional Adversarial Recurrent Training for Algorithm Learning
Quick summary
arXiv:2609.05988v1 Announce Type: cross Abstract: Recurrent reasoning models (RRMs) can solve structured problems, achieving easy-to-hard generalization through iterative computation in hidden space. These models are typically trained with instance-level supervision, which becomes increasingly problematic as task difficulty grows: valid solutions occupy a tiny region of the solution space, while invalid solutions proliferate rapidly. We propose Distributional Adversarial Recurrent Training (DART), a training framework that replaces single-point supervision with a local target distribution arou
Key takeaways
- arXiv:2609.05988v1 Announce Type: cross Abstract: Recurrent reasoning models (RRMs) can solve structured problems, achieving easy-to-hard generalization through iterative computation in hidden space.
- These models are typically trained with instance-level supervision, which becomes increasingly problematic as task difficulty grows: valid solutions occupy a tiny region of the solution space, while invalid solutions proliferate rapidly.
- We propose Distributional Adversarial Recurrent Training (DART), a training framework that replaces single-point supervision with a local target distribution arou
Why it matters
“DART: Distributional Adversarial Recurrent Training for Algorithm Learning” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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