Year: 2026
Type: conference-paper
Abstract: Large language models exhibit strong reasoning capabilities but often require significant computational resources due to verbose, unstructured Chain-of-Thought outputs.Recent approaches guide reasoning length through token penalties or truncation, risking the omission of necessary steps.We posit that conciseness should be an emergent property of structured thought, rather than a result of artificially forced brevity.To this end, we first demonstrate that Attribute-Guided Prompting, a lightweight zero-shot strategy, improves reasoning performance while reducing inference cost.Building on this foundation, we introduce Controlled Attribute-Driven Reasoning Optimization (CLARO), a reinforcement learning framework designed to internalize these benefits.CLARO guides models to embed high-quality structural attributes, such as readability, math density, syntactic compression, and low redundancy, within a user-defined token budget.The proposed method outperforms state-of-the-art baselines across diverse benchmarks, yielding accuracy gains of up to 63.6%, demonstrating that guiding generated output language structure enhances reasoning.Overall, our findings establish that optimizing the thought process structure refines reasoning efficacy, with computational efficiency emerging as a derivative benefit of a clearer thought process.Code and models are available at https://github.com/odedsc/CLARO.
Institutions: Duke University, Princeton University, Apple (United States)
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166856058