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Reinforcement Learning Without Parameter Updates: OpenAI Researcher Proposes New Paradigm Where AI Writes a .py File for Decisions

OpenAI researcher Jiayu Weng proposes Heuristic Learning (HL), a new paradigm that achieves reinforcement learning without neural network training or gradient updates. Instead, it uses GPT-5.4-driven Codex to iteratively edit code as decision policies. HL scored a perfect 864 on Atari Breakout, matched PPO on Atari 57 benchmarks, and achieved state-of-the-art results in continuous robotic control tasks. It offers interpretability, resistance to catastrophic forgetting, and high sample efficiency.

Source量子位Author: 闻乐
Reinforcement Learning Without Parameter Updates: OpenAI Researcher Proposes New Paradigm Where AI Writes a .py File for Decisions
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  • Heuristic Learning (HL) replaces neural network parameters with explicit program code for decision-making
  • Achieves perfect score on Atari Breakout and competitive performance across 57 Atari games
  • Outperforms traditional RL algorithms like PPO in several game and robot simulation tasks
  • Combines code editing with gradient-free learning, enabling explainable and continual learning

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