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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