arXiv:2510.01833v2 Announce Type: replace-cross Abstract: Large language models (LLMs) demonstrate strong reasoning abilities via Chain-of-Thought (CoT), but their token-level generation encourages local decisions and lacks global planning, often leading to redundant or inaccurate reasoning. Existing methods, such as tree-based search and reinforcement learning (RL), attempt to address this issue but incur high computational costs and still struggle to produce reliable reasoning trajectories. To address these challenges, we propose Plan-Then-Action Enhanced Reasoning with Group Relative Policy
The rapid advancement and widespread deployment of large language models are highlighting their inherent limitations in complex reasoning, making enhanced planning architectures a critical next step.
Improving LLM reasoning through better planning directly impacts the capabilities and reliability of autonomous AI systems across various applications, from creative tasks to strategic decision-making.
This research outlines a method to make LLMs more effective and less error-prone in complex tasks, potentially leading to more robust and trustworthy AI applications.
- · AI developers
- · Enterprises adopting AI
- · Software automation
- · AI research institutions
- · Legacy AI solutions
- · Developers relying solely on brute-force CoT
- · Human task performers where AI can substitute
LLMs become more capable of complex, multi-step reasoning with fewer errors and redundance.
The development and deployment of more reliable and autonomous AI agents accelerate across industries.
New forms of white-collar automation and decision-support systems emerge, leading to significant productivity gains and shifts in workforce demands.
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Read at arXiv cs.CL