URPO: A Unified Reward & Policy Optimization Framework for Large Language Models

• URL: http://arxiv.org/abs/2507.17515v1
• Date: Wed, 23 Jul 2025 13:52:27 GMT
• Title: URPO: A Unified Reward & Policy Optimization Framework for Large Language Models
• Authors: Songshuo Lu, Hua Wang, Zhi Chen, Yaohua Tang,
• Abstract summary: We propose a novel framework that unifies instruction-following ("player") and reward modeling ("referee") within a single model and a single training phase.<n>Our method recasts all alignment data-including preference pairs, verifiable reasoning, and open-ended instructions-into a unified generative format.<n>Experiments on the Qwen2.5-7B model demonstrate URPO's superiority.
• Score: 10.511836918064724
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Large-scale alignment pipelines typically pair a policy model with a separately trained reward model whose parameters remain frozen during reinforcement learning (RL). This separation creates a complex, resource-intensive pipeline and suffers from a performance ceiling due to a static reward signal. We propose a novel framework, Unified Reward & Policy Optimization (URPO), that unifies instruction-following ("player") and reward modeling ("referee") within a single model and a single training phase. Our method recasts all alignment data-including preference pairs, verifiable reasoning, and open-ended instructions-into a unified generative format optimized by a single Group-Relative Policy Optimization (GRPO) loop. This enables the model to learn from ground-truth preferences and verifiable logic while simultaneously generating its own rewards for open-ended tasks. Experiments on the Qwen2.5-7B model demonstrate URPO's superiority. Our unified model significantly outperforms a strong baseline using a separate generative reward model, boosting the instruction-following score on AlpacaEval from 42.24 to 44.84 and the composite reasoning average from 32.66 to 35.66. Furthermore, URPO cultivates a superior internal evaluator as a byproduct of training, achieving a RewardBench score of 85.15 and surpassing the dedicated reward model it replaces (83.55). By eliminating the need for a separate reward model and fostering a co-evolutionary dynamic between generation and evaluation, URPO presents a simpler, more efficient, and more effective path towards robustly aligned language models.

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