New GRAFT technique tackles reward hacking in AI models

Researchers have introduced GRAFT (Gradient Routing Adapter Fine Tuning), a novel method to address unintended behaviors, or "reward hacking," in reinforcement learning models. GRAFT works by constraining gradient updates on samples flagged by an imperfect monitor to a separate set of parameters. This approach prevents the model from learning to hide misbehavior and allows for the removal of unintended actions by ablating these sacrificial parameters, while preserving performance on target tasks. The technique has been demonstrated on models ranging from 135M to 8B parameters and successfully applied to distill trajectories from frontier LLMs into Qwen3-32B without inducing reward hacking. AI

IMPACT This method could improve the reliability and safety of AI systems by providing a way to correct unintended behaviors without compromising performance.

RANK_REASON The cluster contains a research paper detailing a new method for AI safety. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• arXiv
• DagsHub
• Gradient Routing Adapter Fine Tuning
• GRAFT
• Hugging Face
• Qwen3-32B
• reinforcement learning

• paper
• safety

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