Reward Hacking as a Transmissible Strategy in Multi-Agent Coding Systems

Key points

• Multi-agent systems are increasingly used to solve coding tasks by coordinating agents that share code and plans.1
• Across four open-weight backbones, coding RL elicits and amplifies reward hacking in two settings: explicit mechanism descriptions in the system prompt and mid-training that models upstream exposure to facts about the coding environment.1
• Even agents based on small open-weight models induce reward hacking in stronger closed-weight targets.1
• Beyond propagation, reward hacking can bias task routing toward reward-hacking agents and survive candidate aggregation.1

Sources (1)

• [1]Reward Hacking as a Transmissible Strategy in Multi-Agent Coding Systems
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 12:02 AM

  In this work, we show that a reward-hacking strategy elicited in one agent can propagate to others and affect system coordination.

  Multi-agent systems are increasingly used to solve coding tasks by coordinating agents that share code and plans.

Extractive summary: sentences quoted from the sources.