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Reasoning Through Execution: Unifying Process and Outcome Rewards for Code Generation

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arxiv 2412.15118 v2 pith:55TREVZF submitted 2024-12-19 cs.CL cs.AIcs.LGcs.SE

Reasoning Through Execution: Unifying Process and Outcome Rewards for Code Generation

Zhuohao Yu , Weizheng Gu , Yidong Wang , Xingru Jiang , Zhengran Zeng , Jindong Wang , Wei Ye , Shikun Zhang This is my paper
classification cs.CL cs.AIcs.LGcs.SE
keywords codeoutcomeprocessreasoningsupervisioncomplexgenerationmodels
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Large Language Models excel at code generation yet struggle with complex programming tasks that demand sophisticated reasoning. To bridge this gap, traditional process supervision relies on learned reward models requiring costly training data and suffering from reward misalignment, while outcome supervision fails for complex tasks needing coordinated intermediate steps. We introduce Outcome Refining Process Supervision, which unifies process and outcome supervision by leveraging executable verification: a tree-structured search framework generates strategic alternatives, profiles execution metrics, and scores candidates via self-critique mechanisms that integrate runtime feedback with reasoning. Experiments across 5 models and 3 benchmarks show consistent gains, with 26.9% higher correctness and 42.2% improved code efficiency. The results demonstrate that ORPS enables LLMs to overcome local optima in code generation, suggesting a promising direction for combining verifiable outcomes with structured reasoning to tackle complex challenges. We open-source at: https://github.com/zhuohaoyu/ORPS

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Cited by 4 Pith papers

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    A survey that organizes existing work on LLM-based agents around code as the central harness, structured in three layers of interfaces, mechanisms, and multi-agent scaling, with applications across domains and listed ...

  3. PYTHALAB-MERA: Validation-Grounded Memory, Retrieval, and Acceptance Control for Frozen-LLM Coding Agents

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  4. From System 1 to System 2: A Survey of Reasoning Large Language Models

    cs.AI 2025-02 accept novelty 3.0

    The survey organizes the shift of LLMs toward deliberate System 2 reasoning, covering model construction techniques, performance on math and coding benchmarks, and future research directions.