Harnesses may be widening LM capability without creating true generalization
a1zhang · x · 2026-07-23
The post argues that the harness around an LM can reduce the burden of generalization: instead of relying only on the model’s internal mechanisms to “grok everything,” the setup can make the model see at test time the same kinds of things it saw during evaluation.
A reply pushes back on the stronger claim that harnesses help generalization in the compositional sense. The counterpoint is that harnesses likely expand the class of tasks LMs can solve, especially in code-like settings, but that success may come from scaffolding rather than true generalization.
Related event: LLM Generalization Debate: Intrinsic Model or Harness Contribution?(11 posts)→
More from Research
- Marigold V2 Hits New SOTA in Monocular Depth Estimation with Single-Step Diffusion Transformers — AntonObukhov1 · 2026-09-11
- How AI Agents Turn Experience Into Lasting Gains: A Guide to Recursive Self-Improvement — Roger_M_Taylor · 2026-09-11
- Joshua Gans: ChatGPT 5.2 Pro wrote a full paper in 19 minutes, but quality ideas still matter — joshgans · 2026-09-11
- Four-Color Theorem Gets a Rare New Proof, Revisiting Its Controversial 1970s Computer-Assisted Solution — soumitrashukla9 · 2026-09-11
- The Roadmap of Mathematics for Machine Learning: Linear Algebra, Calculus, Probability — TivadarDanka · 2026-09-11
- GEVIBench launches as a comprehensive benchmark for comparing voltage indicators — drmichaellevin · 2026-09-11