无需人类编程，自动发现可解释的排序算法
AlgoPilot: Fully Autonomous Program Synthesis Without Human-Written Programs
- 用强化学习+轨迹语言模型从零生成算法
- 在排序任务中自动生成冒泡排序等经典算法
- 适合对自主算法生成感兴趣的开发者
程序合成传统依赖人工提供的规范、示例或先验知识来生成功能性算法。现有方法或模仿人类编写的算法，或仅解决特定任务而无法生成可复用的程序逻辑，限制了其创造新算法的能力。我们提出 AlgoPilot，一种完全自动化的程序合成方法，无需人类编写的程序或轨迹。AlgoPilot 利用强化学习（RL）并由轨迹语言模型（TLM）引导，从零开始合成算法。TLM 在随机生成的 Python 函数轨迹上训练，作为强化学习过程中的软约束，使生成序列与可能代表有效算法的模式对齐。以排序为例，AlgoPilot 展示了其生成可解释为经典算法（如冒泡排序）轨迹的能力，且不依赖任何先验算法知识。本工作建立了算法发现的新范式，为未来自主程序合成的发展奠定基础。
原文摘要 · Abstract (English)
Program synthesis has traditionally relied on human-provided specifications, examples, or prior knowledge to generate functional algorithms. Existing methods either emulate human-written algorithms or solve specific tasks without generating reusable programmatic logic, limiting their ability to create novel algorithms. We introduce AlgoPilot, a groundbreaking approach for fully automated program synthesis without human-written programs or trajectories. AlgoPilot leverages reinforcement learning (RL) guided by a Trajectory Language Model (TLM) to synthesize algorithms from scratch. The TLM, trained on trajectories generated by random Python functions, serves as a soft constraint during the RL process, aligning generated sequences with patterns likely to represent valid algorithms. Using sorting as a test case, AlgoPilot demonstrates its ability to generate trajectories that are interpretable as classical algorithms, such as Bubble Sort, while operating without prior algorithmic knowledge. This work establishes a new paradigm for algorithm discovery and lays the groundwork for future advancements in autonomous program synthesis.
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