I use GPT to generate a policy optimization algorithm [pdf] (github.com)

A developer/researcher published a short paper and accompanying repo showing they used GPT as the creative engine to generate a policy‑optimization algorithm and implementation. The submission documents the prompt engineering, iterative conversations with the model, the generated pseudocode and code, and the author's debugging and refinement steps that turned GPT’s output into a runnable algorithm. The artifact (hacpo) is presented as an experiment in using large language models to accelerate algorithm design rather than a claim of a breakthrough new RL method. The piece is significant because it illustrates, end‑to‑end, how LLMs can contribute to core research tasks: proposing algorithmic structure, writing boilerplate and experimental code, and suggesting evaluation plans. Key technical takeaways include the importance of careful prompt design and human-in-the-loop verification, how GPT’s suggestions were adapted into a policy‑optimization loop (surrogate objectives, gradient steps, and engineering fixes), and that empirical validation remains essential to filter hallucinations or subtle bugs. The work highlights practical implications for AI/ML: faster prototyping of algorithms, increased accessibility for researchers, and new reproducibility and safety challenges—underscoring that LLMs are powerful design assistants but cannot yet replace rigorous theoretical and empirical vetting.