Researchers Show Reasoning Models Beat Standard Transformers on Scaling to Hard Tasks
Specifically training reasoning language models yields better scaling and generalization on complex tasks than standard Transformer architectures. The improved performance stems from a learning mechanism that reduces novel problems to locally in-distribution observations for the underlying neural network.
The researchers cautioned that the findings represent early results and are not intended as universal conclusions. They noted the approach highlights how reinforcement learning can leverage higher-level inductive biases to improve compositional generalization as models grow in scale.
From the sources (4 posts)
@lateinteractionRT @a1zhang: Transformers struggle to generalize to tasks they were not explicitly trained on. Instead, we propose in 2026 that it is the j…
@lateinteractionThe "harness" is starting to blur with the neural architecture, in terms of who carries the inductive biases that unlock generalization. We show that training RLMs specifically is far superior at scaling and generalization to harder tasks
@lateinteractionIn a way, this is not new. Reasoning models are a harness too. But we identify a key property that makes harnesses improve learning efficiency: their ability to learn to reduce novel problems to locally in-distribution observations for the
@scaling01RT @a1zhang: Transformers struggle to generalize to tasks they were not explicitly trained on. Instead, we propose in 2026 that it is the j…