MIT PhD claims harness design can make RLMs generalize from short tasks to 100× longer ones
a1zhang · x · 2026-07-21
- MIT PhD student Alex Zhang argues that the harness can do much of the generalization work in recursive language models (RLMs): if the surrounding design is clever, some scaling gains can be had “for free.”
- The key observation is that models trained only on short tasks can transfer surprisingly well to much longer problems in a similar domain, sometimes at 100× longer context lengths.
- In the quoted explanation, the claim is that a well-designed harness effectively induces structural equivalence between different trajectories, so the transformer does not need to learn all the generalization itself.
- The post frames this as a shift in where generalization lives: less in the base model, more in the orchestration/composition layer.
Related event: Research: Harness Drives Generalization in Reinforcement Language Models(9 posts)→
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