Can Reasoning Path still be Effective as Input? Bridging Post-Reasoning to Chain-of-Thought Compression
arXiv:2510.08647v2 Announce Type: replace-cross Abstract: Recent developments have enabled advanced reasoning in Large Language Models (LLMs) via long Chain-of-Thought (CoT), trading efficiency during inference for performance. Existing works focus on compressing generated CoT in reasoning, which impairs the necessary information for deriving the correct answer. In this work, we propose post-reasoning, a reasoning paradigm that takes CoT as a part of context to simplify the reasoning task for LLMs. We find that post-reasoning significantly reduces the generation length of LLMs, but its effecti
The proliferation of advanced LLMs and their computational demands makes efficiency in reasoning a critical and immediate concern.
This research addresses the trade-off between LLM reasoning performance and efficiency, a key bottleneck for wider deployment and cost-effectiveness of AI applications.
The proposed 'post-reasoning' paradigm offers a method to achieve similar reasoning capabilities with significantly reduced computational cost and generation length for LLMs.
- · LLM developers
- · Cloud providers dependent on LLM usage
- · AI application integrators
- · Researchers in AI efficiency
- · Inefficient LLM architectures
- · Companies with high inference costs
Reduced operational costs and faster inference for advanced AI models.
Accelerated deployment of complex AI agents and services in real-world scenarios due to improved scalability.
A potential shift in AI development focus towards optimising post-generation reasoning rather than solely pre-computation or model size.
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Read at arXiv cs.AI