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论文提出 Adversarial Heuristic Learning (AHL) 范式，并介绍 AAArena 基准，包含 12 个真实对抗游戏和 1,920 个历史人类程序，用于评估 AI 智能体能否通过修改可执行策略在长期游戏竞赛中达到顶尖水平。结果显示 Opus 5.5 搭配 Claude Code 获得 6 枚金牌，但在规则复杂的游戏中表现较弱。
The paper presents AHL Studio, a tool using agentic heuristic learning to create executable, inspectable policies for human activity recognition without traditional training, targeting edge deployment.
The article proposes using coding agents to maintain and iterate a system of programmatic strategies to replace neural network gradient updates. This approach achieved baseline performance in Deep RL tests and is considered a potential new paradigm following pre-training and RLHF.
Former OpenAI researcher Jiayi Weng proposed a new paradigm called "Heuristic Learning", which uses large language models to generate and iteratively modify Python code to solve reinforcement learning tasks. Knowledge is stored in interpretable code rather than neural network parameters, effectively avoiding catastrophic forgetting. It has achieved excellent results on Atari and MuJoCo benchmarks and the code has been open-sourced.