Self-improving AI system that writes better code by learning from its own mistakes.
O1-CODER is a novel framework that replicates OpenAI's O1 model specifically for coding tasks. It combines reinforcement learning with Monte Carlo Tree Search to enhance System-2 thinking capabilities, focusing on generating high-quality code through structured reasoning and automated test case validation.
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https://arxiv.org/abs/2412.00154
Original Problem 🤔:
Traditional LLMs lack systematic reasoning capabilities for complex coding tasks, primarily exhibiting fast, intuitive responses without intermediate reasoning steps.
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Solution in this Paper 🛠:
→ The framework introduces a Test Case Generator (TCG) that automatically creates standardized test cases for code validation.
→ It employs Monte Carlo Tree Search to generate code with detailed reasoning processes, including validity indicators.
→ The system uses a "think before acting" approach where it first generates pseudocode before producing executable code.
→ A Process Reward Model evaluates the quality of intermediate reasoning steps during code generation.
→ The framework implements self-play reinforcement learning, continuously generating new reasoning data to improve model performance.
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Key Insights 💡:
→ Pseudocode-based reasoning significantly improves code generation quality when reasoning is correct
→ Combining supervised fine-tuning with Direct Preference Optimization enhances test case generation
→ Self-play reinforcement learning creates a continuous improvement cycle for both reasoning and code generation
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Results 📊:
→ Test Case Generator achieved 89.2% pass rate after DPO, up from 80.8% after initial fine-tuning
→ Qwen2.5-Coder-7B showed 74.9% Average Sampling Pass Rate with pseudocode approach, a 25.6% improvement
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