TL;DR: Too Long, Do Re-weighting for Effcient LLM Reasoning Compression
Authors: name: Zhong-Zhi Li, name: Xiao Liang, name: Zihao Tang, name: Lei Ji, name: Peijie Wang, name: Haotian Xu, name: Xing W, name: Haizhen Huang, name: Weiwei Deng, name: Ying Nian Wu, name: Yeyun Gong, name: Zhijiang Guo, name: Xiao Liu, name: Fei Yin, name: Cheng-Lin Liu

Large Language Models (LLMs) have recently achieved remarkable progress by
leveraging Reinforcement Learning and extended Chain-of-Thought (CoT)
techniques. However, the challenge of performing efficient language
reasoning--especially during inference with extremely long outputs--has drawn
increasing attention from the research community. In this work, we propose a
dynamic ratio-based training pipeline that does not rely on sophisticated data
annotations or interpolation between multiple models. We continuously balance
the weights between the model's System-1 and System-2 data to eliminate
redundant reasoning processes while preserving the model's reasoning
capability. We validate our approach across models on DeepSeek-R1-Distill-7B
and DeepSeek-R1-Distill-14B and on a diverse set of benchmarks with varying
difficulty levels. Our method significantly reduces the number of output tokens
by nearly 40% while maintaining the accuracy of the reasoning. Our code and
data will be available soon.