Focused Chain-of-Thought: Efficient LLM Reasoning via Structured Input Information
Recent large language models achieve strong reasoning performance by generating detailed chain-of-thought traces, but this often leads to excessive token use and high inference latency. Existing efficiency approaches typically focus on model-centric interventions, such as reinforcement learning or supervised fine-tuning, to reduce verbosity. In contrast, we propose a training-free, input-centric approach. Inspired by cognitive psychology, we introduce Focused Chain-of-Thought (F-CoT), which separates information extraction from the reasoning process. F-CoT first organizes the essential information from a query into a concise, structured context and then guides the model to reason exclusively over this context. By preventing attention to irrelevant details, F-CoT naturally produces shorter reasoning paths. On arithmetic word problems, F-CoT reduces generated tokens by 2-3x while maintaining accuracy comparable to standard zero-shot CoT. These results highlight structured input as a simple yet effective lever for more efficient LLM reasoning.
692d08524397b1ec214f67ab
Lukas Struppek
692d08524397b1ec214f67ac
Dominik Hintersdorf
692d08524397b1ec214f67ad
Hannah Struppek
692d08524397b1ec214f67ae
Daniel Neider
692d08524397b1ec214f67af
Kristian Kersting
Focused Chain-of-Thought: Efficient LLM Reasoning via Structured Input Information
Recent large language models achieve strong reasoning performance by generating detailed chain-of-thought traces, but this often leads to excessive token use and high inference latency. Existing efficiency approaches typically focus on model-centric interventions, such as reinforcement learning or supervised fine-tuning, to reduce verbosity. In contrast, we propose a training-free, input-centric approach. Inspired by cognitive psychology, we introduce Focused Chain-of-Thought (F-CoT), which separates information extraction from the reasoning process. F-CoT first organizes the essential information from a query into a concise, structured context and then guides the model to reason exclusively over this context. By preventing attention to irrelevant details, F-CoT naturally produces shorter reasoning paths. On arithmetic word problems, F-CoT reduces generated tokens by 2-3x while maintaining accuracy comparable to standard zero-shot CoT. These results highlight structured input as a simple yet effective lever for more efficient LLM reasoning.
F-CoT, an input-centric approach inspired by cognitive psychology, reduces token usage in large language models by structuring context and focusing reasoning, maintaining accuracy on arithmetic problems.
Focused Chain-of-Thought (F-CoT) cognitive psychology chain-of-thought traces reinforcement learning supervised fine-tuning attention reasoning paths