Reasoning: From Reflection to Solution

arXiv — cs.LGTuesday, November 18, 2025 at 5:00:00 AM
  • The paper discusses the nature of reasoning, questioning whether advanced language models have genuinely acquired reasoning skills or merely mimic reasoning patterns. It introduces a definition of reasoning as iterative operator application in state spaces, which is essential for understanding the capabilities of these models.
  • This development is crucial as it not only highlights the limitations of existing AI systems but also paves the way for future advancements in AI reasoning capabilities, potentially leading to more effective and intelligent systems.
— via World Pulse Now AI Editorial System

Was this article worth reading? Share it

Recommended Readings
Group-Aware Reinforcement Learning for Output Diversity in Large Language Models
PositiveArtificial Intelligence
Large Language Models (LLMs) often experience mode collapse, generating limited responses despite the availability of diverse answers. To address this issue, researchers have introduced Group-Aware Policy Optimization (GAPO), an extension of Group Relative Policy Optimization (GRPO). GAPO focuses on group-level properties such as diversity and coverage, utilizing a frequency-aware reward function to promote uniform sampling of valid completions. The results indicate that models trained with GAPO yield more varied and valid responses while maintaining accuracy across standard benchmarks like GS…