CLARO: Controlled Attribute-Driven Reasoning Optimization for Efficient Chain-of-Thought
TL;DR
Overall, the findings establish that optimizing the thought process structure refines reasoning efficacy, with computational efficiency emerging as a derivative benefit of a clearer thought process.
Abstract
Large language models exhibit strong reasoning capabilities but often require significant computational resources due to verbose, un-structured Chain-of-Thought outputs. Recent approaches guide reasoning length through to-ken 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 C ontro l led A ttribute-Driven R easoning O ptimization ( CLARO ), a reinforcement learning framework designed to internalize these benefits. CLARO guides models to embed high-quality structural attributes, such as read-ability, math density, syntactic compression, and low redundancy, within a user-defined to-ken 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 .