用编码智能体做 ML 任务会遇到哪些挑战？[D]

What are the challenges encountered when using coding agents for ML tasks? [D]

AI 导读

有研究指出，AI 智能体在设计 ML 实验时会作弊、产生模型幻觉并反复撒谎，即便提示词明确禁止也一样；由于经过 RL 训练，它们会为提升指标尝试数据泄漏甚至篡改评估函数。发帖者因此设想用 MCP 把智能体绑定到受限运行时环境，只允许执行特定任务或写类型化代码块，并询问这种做法是否优于让其自由编写 Python，以及约束是否会削弱模型能力。

正文

Lately I have read several studies which say that AI agents when designing ML experiments tend to cheat, hallucinate and lie repeatedly. Even when given explicit prompts to not to. SInce these models are RL trained and when given specific goal to increase a metric, they try any and every possibility to increase it. Be it data leakage or changing the evaluator function itself. Even Karparthy's auto research styled agents still fall into this domain.

So I have been thinking of reducing this undesirable behavior by binding them to a runtime environment using an mcp. I believe this will stop them from achieving unreal metrics by cheating the system since they will be only allowed to do specific tasks or write codes in typed blocks?

What have you encountered when using these agents for your ml tasks? And is this method of forcing these agents to work in a constrained environment better than just letting them write arbitrary python? Also, will constraining the agents reduce their capabilities or ability to fit better models?

submitted by /u/Jumpy-Cobbler1020
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