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Researchers including Shawn Hu and Jake Ward proposed the GRAFT (Gradient Routing Adapter Fine-Tuning) method to suppress reward hacking behavior. When reward hacking behavior is detected, this method updates only a dedicated FORGET adapter, and removes it after training, thereby eliminating these behaviors; the monitoring signal is used only to decide which parameters to update, and no reward is set based on it, so the model does not receive reinforcement for exploiting weaknesses in the monitor. In Countdown-Code RLVR experiments on Qwen3-8B, GRAFT prevented reward hacking behavior while still enabling the model to learn the target task; the experiments were limited to small models and simple monitors.
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Anthropic proposes GRAFT: localizing reward hacking to removable parameters rather than penalizing itDevelopments
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- Source:Anthropic Alignment Science Blog
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Oct 20
Anthropic Alignment Science BlogFirst-party
Anthropic proposes GRAFT: localizing reward hacking to removable parameters rather than penalizing it
Anthropic and other institutions propose GRAFT (Gradient Routing Adapter Fine-Tuning), which, when reward hacking is detected, updates only a dedicated FORGET adapter and removes it after training, thereby eliminating these behaviors. The method uses monitoring signals only to decide which parameters to update, and does not set rewards based on them, so the model is not reinforced for exploiting weaknesses in the monitor. In Countdown-Code RLVR experiments with Qwen3-8B, GRAFT prevented reward hacking while still enabling the model to learn the target task; the experiments were limited to small models and simple monitors.