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README.md
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IQuest-Coder-V1 is a new family of code large language models (LLMs) designed to advance autonomous software engineering and code intelligence. Built on the innovative code-flow multi-stage training paradigm, IQuest-Coder-V1 captures the dynamic evolution of software logic, delivering state-of-the-art performance across critical dimensions:
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- **Code-Flow Training Paradigm**: Moving beyond static code representations, our models learn from repository evolution patterns, commit transitions, and dynamic code transformations to understand real-world software development processes.
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- **Dual Specialization Paths**: Bifurcated post-training delivers two specialized variants—Thinking models (utilizing reasoning-driven RL for complex problem-solving) and Instruct models (optimized for general coding assistance and instruction-following).
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- **Efficient Architecture**: The IQuest-Coder-V1-Loop variant introduces a recurrent mechanism that optimizes the trade-off between model capacity and deployment footprint. The 7B and 14B models adopt shallow architectures for faster inference speed.
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| **BigCodeBench** | 0.0 | - |
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| **FullStackBench** | 0.0 | - |
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| **CruxEval** | 0.0 | - |
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| **LiveCodeBench** |
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| **Aider-Polyglot** | 0.95 | 0.85 |
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| **Mercury** | 0.2 | 0.85 |
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| **Bird** | 0.2 | 0.95 |
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IQuest-Coder-V1 is a new family of code large language models (LLMs) designed to advance autonomous software engineering and code intelligence. Built on the innovative code-flow multi-stage training paradigm, IQuest-Coder-V1 captures the dynamic evolution of software logic, delivering state-of-the-art performance across critical dimensions:
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- **Performance**: Achieves leading results on SWE-Bench Verified (76.2%), BigCodeBench (49.9%), LiveCodeBench v6 (81.1%), and other major coding benchmarks, surpassing competitive models across agentic software engineering, competitive programming, and complex tool use.
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- **Code-Flow Training Paradigm**: Moving beyond static code representations, our models learn from repository evolution patterns, commit transitions, and dynamic code transformations to understand real-world software development processes.
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| 67 |
- **Dual Specialization Paths**: Bifurcated post-training delivers two specialized variants—Thinking models (utilizing reasoning-driven RL for complex problem-solving) and Instruct models (optimized for general coding assistance and instruction-following).
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| 68 |
- **Efficient Architecture**: The IQuest-Coder-V1-Loop variant introduces a recurrent mechanism that optimizes the trade-off between model capacity and deployment footprint. The 7B and 14B models adopt shallow architectures for faster inference speed.
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| 206 |
| **BigCodeBench** | 0.0 | - |
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| 207 |
| **FullStackBench** | 0.0 | - |
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| 208 |
| **CruxEval** | 0.0 | - |
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| 209 |
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| **LiveCodeBench** | 0.6 | 0.95 |
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| 210 |
| **Aider-Polyglot** | 0.95 | 0.85 |
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| 211 |
| **Mercury** | 0.2 | 0.85 |
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| 212 |
| **Bird** | 0.2 | 0.95 |
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