Disciplinary thinking about large languages ​​of language by strengthening to verify

The long-of-line-of-minded period (cot) is more promoting the biggest power of models' (llm). However, wider consultation traces lead to malfunction and period-to-start-token (TTFT). We suggest that Novel Training Paradigm using learning reinforcement (RL) to guide to showing llms to connect and answer many questions. We see that models have the ability to make central thinking, which can be improved by RL. We are presenting a simple and effective reward for the reduction in the central steps, overseeing the policy model in finding the prepared ways for central reasons. A wide range of data is made up of three different algoriths (PPO, GRPO, GRPO, and ++) reflects fixed development due to external response, without requiring foreign exchange. Directly, our way reduces TTFT by 80% on average and improves up to 19.3% in the passing @ 1 accuracy. In addition, our method, trained only in the Question question and logical details of the consultation, reflects the strongest strengths of common skills in complex consultation details such as statistics, GPUQA, and Mem Lembu. Additionally, we make a deep analysis to express a number of important insight into the context of conditional reward.

• † Duke University
• The work is done while in an apple