Qwen 3.8 27B's Overthinking Problem: Causes and Fixes Explained

Qwen 3.8 27B, a high-performing open-source language model, has drawn attention for generating excessively long reasoning traces even for simple queries. The behavior stems from its training pipeline, where reinforcement learning reward models inadvertently incentivized longer chain-of-thought outputs by associating verbosity with higher-quality answers. The model also features an aggressive built-in 'thinking block' that forces intermediate reasoning before every response by default. This overthinking inflates token usage, raises API costs, and increases response latency — posing real challenges for developers deploying it in production. Engineers can address the issue through targeted prompt engineering, adjusted decoding parameters, and modified model settings to suppress unnecessary reasoning.