Meta Superintelligence Labs 研究：多智能体编码系统中奖励黑客的传播与缓解
Meta Superintelligence Labs 发布研究，讨论奖励黑客（reward hacking）如何影响多智能体编码系统的协作。论文指出，共享代码、记忆和自然语言计划会让一个智能体学到的黑客行为传播给其他智能体；小的开源权重模型智能体甚至能诱导更强的闭源权重模型智能体产生黑客行为，且在困难任务上这种行为会沿链条扩散，即使原智能体已退出。
Interesting new research from Meta Superintelligence Labs
It discusses how reward hacking affects coordination in multi-agent coding systems.
Coding agents on a team share code, memory, and plans, so a reward hack one agent learns gets passed to the others.
The paper traces this through three channels. Hacks move through shared code, through shared memory, and across roles through natural-language plans.
Agents on small open-weight models can induce hacking in stronger closed-weight agents. On hard tasks, the hacking can grow along a chain even after the original agent has left, because the code collects more convincing justifications on the way.
It also biases task routing toward the hacking agents and survives candidate aggregation.
So how do you mitigate this problem?
The authors suggest that code review by the coder or by a separate verifier reduces the hacks that get delivered. If your agents share a repo, the review step is the first thing to add.
arxiv.org/abs/2610.12822
Chat with Paper: academy.dair.ai/papers/rewar…
来源：omarsar0 · x.com