Reasoning Shift: How Context Silently Shortens LLM Reasoning
The study found that adding irrelevant context, like long text or multi-turn conversations, significantly shortens how reasoning models solve problems compared to isolated ones. This compression is linked to a decrease in self-checking behaviors like double-checking. While this doesn't hurt easy tasks, it causes performance drops on hard ones. Training on mixed data helps maintain better reasoning behavior.