LLM Progress Non-linear: Hard Task Gains May Stem from Ceiling Effects
LChoshen · x · 2026-08-09
The researcher points out that progress in large language models (LLMs) is no longer linear. New models improve more than expected on hard tasks, while gains on easier tasks lag behind.
This raises questions about whether model capability development is universally generalizable. According to related research, this apparent shift towards hard tasks might largely be explained by ceiling effects in metrics rather than a qualitative change in the models. Furthermore, newer models are often paired with newer agentic harnesses, making it difficult to isolate whether the gains come from the model itself or the scaffolding.
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