Reasoning models may work as learning harnesses
lateinteraction · x · 2026-07-20
The author argues that reasoning models are not fundamentally new, but a kind of harness. The important property is that they can convert novel problems into observations that are locally in-distribution for the base neural network, which improves learning efficiency.
The post points readers to Alex’s thread and the accompanying blog post, saying the empirical results are compelling. The key claim is that RLMs seem especially good at this reduction step, which may explain part of their usefulness.
Related event: Reasoning Models Act as Learning Harnesses(2 posts)→
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