MURPHY: Multi-Turn GRPO for Self Correcting Code Generation

• URL: http://arxiv.org/abs/2511.07833v1
• Date: Wed, 12 Nov 2025 01:23:09 GMT
• Title: MURPHY: Multi-Turn GRPO for Self Correcting Code Generation
• Authors: Chanakya Ekbote, Vijay Lingam, Behrooz Omidvar-Tehrani, Jun Huan, Sujay Sanghavi, Anoop Deoras, Stefano Soatto,
• Abstract summary: Murphy is a multi-turn reflective optimization framework that extends GRPO by incorporating iterative self-correction during training.<n>We show that Murphy consistently improves performance, achieving up to a 8% relative gain in pass@1 over GRPO, on similar compute budgets.
• Score: 55.66642560374686
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a powerful framework for enhancing the reasoning capabilities of large language models (LLMs). However, existing approaches such as Group Relative Policy Optimization (GRPO) and its variants, while effective on reasoning benchmarks, struggle with agentic tasks that require iterative decision-making. We introduce Murphy, a multi-turn reflective optimization framework that extends GRPO by incorporating iterative self-correction during training. By leveraging both quantitative and qualitative execution feedback, Murphy enables models to progressively refine their reasoning across multiple turns. Evaluations on code generation benchmarks with model families such as Qwen and OLMo show that Murphy consistently improves performance, achieving up to a 8% relative gain in pass@1 over GRPO, on similar compute budgets.

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