Posterior-GRPO: Rewarding Reasoning Processes in Code Generation

• URL: http://arxiv.org/abs/2508.05170v1
• Date: Thu, 07 Aug 2025 09:04:10 GMT
• Title: Posterior-GRPO: Rewarding Reasoning Processes in Code Generation
• Authors: Lishui Fan, Yu Zhang, Mouxiang Chen, Zhongxin Liu,
• Abstract summary: Reinforcement learning has significantly advanced code generation for large language models.<n>Current paradigms rely on outcome-based rewards from test cases, neglecting the quality of the intermediate reasoning process.<n>We introduce a unified framework that can effectively incorporate the quality of the reasoning process during RL.
• Score: 7.893963076886232
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement learning (RL) has significantly advanced code generation for large language models (LLMs). However, current paradigms rely on outcome-based rewards from test cases, neglecting the quality of the intermediate reasoning process. While supervising the reasoning process directly is a promising direction, it is highly susceptible to reward hacking, where the policy model learns to exploit the reasoning reward signal without improving final outcomes. To address this, we introduce a unified framework that can effectively incorporate the quality of the reasoning process during RL. First, to enable reasoning evaluation, we develop LCB-RB, a benchmark comprising preference pairs of superior and inferior reasoning processes. Second, to accurately score reasoning quality, we introduce an Optimized-Degraded based (OD-based) method for reward model training. This method generates high-quality preference pairs by systematically optimizing and degrading initial reasoning paths along curated dimensions of reasoning quality, such as factual accuracy, logical rigor, and coherence. A 7B parameter reward model with this method achieves state-of-the-art (SOTA) performance on LCB-RB and generalizes well to other benchmarks. Finally, we introduce Posterior-GRPO (P-GRPO), a novel RL method that conditions process-based rewards on task success. By selectively applying rewards to the reasoning processes of only successful outcomes, P-GRPO effectively mitigates reward hacking and aligns the model's internal reasoning with final code correctness. A 7B parameter model with P-GRPO achieves superior performance across diverse code generation tasks, outperforming outcome-only baselines by 4.5%, achieving comparable performance to GPT-4-Turbo. We further demonstrate the generalizability of our approach by extending it to mathematical tasks. Our models, dataset, and code are publicly available.

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