@lateinteraction: The "harness" is starting to blur with the neural architecture, in terms of who carries the inductive biases that unloc…

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rlms transformers generalization inductive-biases scaling composition

Summary

Researchers propose that the harness (training setup) should carry inductive biases for generalization, showing that training RLMs is far superior to vanilla Transformers for scaling and generalization to harder tasks.

The "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 than training vanilla Transformers.
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Cached at: 07/20/26, 05:30 PM

The “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 than training vanilla Transformers.

alex zhang (@a1zhang): Transformers struggle to generalize to tasks they were not explicitly trained on. Instead, we propose in 2026 that it is the job of the harness to generalize through composition.

We observe a powerful property when training RLMs: for tasks with shared structure that look

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