AlgoPilot: Fully Autonomous Program Synthesis Without Human-Written Programs

• URL: http://arxiv.org/abs/2501.06423v1
• Date: Sat, 11 Jan 2025 03:29:14 GMT
• Title: AlgoPilot: Fully Autonomous Program Synthesis Without Human-Written Programs
• Authors: Xiaoxin Yin,
• Abstract summary: We introduce AlgoPilot, a groundbreaking approach for fully automated program synthesis without human-written programs or trajectories. AlgoPilot leverages reinforcement learning guided by a Trajectory Language Model (TLM) to synthesize algorithms from scratch. This work establishes a new paradigm for algorithm discovery and lays the groundwork for future advancements in autonomous program synthesis.
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• Abstract: 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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