Can Reasoning Path still be Effective as Input? Bridging Post-Reasoning to Chain-of-Thought Compression

📰 ArXiv cs.AI

Learn how to improve Large Language Models' reasoning using post-reasoning and Chain-of-Thought compression, and why it matters for efficient inference

advanced Published 4 Jun 2026

Action Steps

Apply post-reasoning to simplify the reasoning task for LLMs
Use Chain-of-Thought compression to reduce inference time
Evaluate the effectiveness of post-reasoning as input for LLMs
Compare the performance of LLMs with and without post-reasoning
Configure LLMs to incorporate post-reasoning and CoT compression for improved efficiency

Who Needs to Know This

NLP researchers and engineers working with Large Language Models can benefit from this technique to enhance model performance and efficiency

Key Insight

💡 Post-reasoning can be an effective input for LLMs, enabling advanced reasoning while reducing inference time

Key Takeaways

Learn how to improve Large Language Models' reasoning using post-reasoning and Chain-of-Thought compression, and why it matters for efficient inference

Full Article

Title: Can Reasoning Path still be Effective as Input? Bridging Post-Reasoning to Chain-of-Thought Compression

Abstract:
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 LL

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