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arXiv · 2605.01111

When Less is Enough: Efficient Inference via Collaborative Reasoning

Yilei Chen·Sharut Gupta·Yannis Paschalidis·Ayush Sekhari·Aldo Pacchiano
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Abstract

In this work, we introduce DUET (Dual-model Efficient Two-stage inference), a collaborative inference framework in which a capable model and a lightweight model work together to solve a task. Relying on a single large model to perform end-to-end reasoning and prediction often incurs substantial inference cost. In contrast, DUET decomposes inference into two stages: the capable model produces a reasoning signal, and the lightweight model interprets this signal to generate the final answer, allowing reasoning-intensive computation to be handled by the capable model while non-reasoning-intensive components are delegated to the lightweight model without sacrificing task performance. To achieve this objective, we propose a length-penalized joint training objective that encourages the capable model to transmit only the information that is sufficient for the lightweight model to solve the task. As a result, DUET maintains strong reasoning performance with substantially lower inference cost than end-to-end inference using a large model alone, saving up to 60% of the large model's output tokens on challenging reasoning benchmarks, including AIME and GPQA.

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Yilei Chen, Sharut Gupta, Yannis Paschalidis, Ayush Sekhari, Aldo Pacchiano. 2026-05-01. When Less is Enough: Efficient Inference via Collaborative Reasoning. https://arxiv.org/abs/2605.01111

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