Controlling Reasoning Effort in LLMs
SMRTR summary
Modern AI reasoning models, like DeepSeek-R1 and OpenAI's GPT-5.6, can now adjust how hard they "think" before answering. By training models with different token-cost penalties tied to effort labels, developers teach them to spend fewer or more tokens depending on the requested effort level, directly trading speed and cost for accuracy.
SMRTR provides this summary for quick context. The original article belongs to Hacker News.
Read the original article