"Outcome-Refining Process Supervision for Code Generation"

Code generation gets smarter when models can explore multiple solutions and learn from their mistakes with Tree-structured reasoning

This paper introduces a framework called Outcome-Refining Process Supervision (ORPS) that helps LLMs write better code by treating outcome refinement as a supervised process, using execution signals and tree-structured exploration.

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https://arxiv.org/abs/2412.15118

🤖 Original Problem:

LLMs struggle with complex programming tasks requiring deep algorithmic reasoning. Current approaches using process supervision need expensive training data and suffer from unreliable evaluation.

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🔧 Solution in this Paper:

→ ORPS treats outcome refinement itself as the process to supervise, combining theoretical understanding with practical implementation.

→ Uses tree-structured exploration instead of linear Chain-of-Thought, maintaining multiple solution paths simultaneously.

→ Leverages concrete execution signals to ground the supervision without needing specially trained reward models.

→ Implements self-critic mechanism where model acts as both programmer and critic, providing detailed analysis before making judgments.

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💡 Key Insights:

→ Providing sufficient reasoning space is more crucial than model size for complex programming

→ Combining execution feedback with self-critique creates more reliable verification than traditional reward models

→ Tree-structured exploration enables discovery of fundamentally different algorithmic strategies

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📊 Results:

→ Achieved 26.9% average increase in correctness across 3 datasets and 5 models

→ Reduced running time by 42.2% on average

→ Even smaller models like Qwen-7B achieved 80% Pass@1 when given sufficient reasoning space

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