Thoughts-as-Planning: Latent World Models for Chain-of-Thoughts Optimization via Reinforcement Planning
arXiv:2605.28842v1 Announce Type: cross Abstract: The success of large language models (LLMs) across diverse NLP tasks has elevated the importance of reasoning chain optimization as a critical step in aligning model behavior with task objectives. Existing reasoning chain tuning methods often rely on black-box heuristics or gradient-free search, which lack interpretability, generalization, and sample efficiency. In this work, we introduce \textbf{Thoughts-as-Planning}, a novel framework that formalizes reasoning chain optimization as a sequential decision-making process over a latent semantic s
The rapid advancement and widespread adoption of Large Language Models necessitate more robust and interpretable methods for optimizing their reasoning capabilities, moving beyond black-box approaches.
Improving the optimization of reasoning chains in LLMs is crucial for developing more reliable, controllable, and generalizable AI systems, vital for complex decision-making and automation.
This framework offers a more systematic and interpretable approach to AI reasoning optimization, potentially leading to more efficient development and deployment of agentic AI systems.
- · AI researchers
- · Developers of autonomous AI agents
- · SaaS companies leveraging advanced AI
- · Industries requiring reliable AI decision-making
- · Developers reliant on black-box heuristics for AI optimization
- · Companies with less sophisticated AI development pipelines
The ability to optimize AI reasoning chains more effectively accelerates the development of advanced AI agents.
More reliable AI agents could lead to significant collapse of certain white-collar workflows, increasing automation across sectors.
The widespread deployment of highly optimized AI agents could fundamentally alter economic structures by increasing productivity and reducing the need for human input in many cognitive tasks.
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Read at arXiv cs.AI