Post-training case study: Reaching frontier-level attack resilience with Alice

AI21 Labs and Alice post-trained NVIDIA’s Nemotron 3.5 Super VL over three weeks to improve enterprise-agent resistance to indirect prompt injection and other malicious requests while preserving task completion. Using Alice’s simulated Slack and GitHub reinforcement-learning environments, the team first applied supervised fine-tuning with successful trajectories rewritten in Nemotron’s own style to reduce distribution drift, repetitive reasoning, and overfitting from a relatively small dataset. It then used a modified GRPO online reinforcement-learning approach that removed standard-deviation reward normalization and added length and repetition penalties to better handle dense rewards and control excessively long outputs. Validation scores rose from 35.19 for the base model to 76.2 after training, while out-of-distribution performance on Alice’s CHASM security benchmark increased from 7.54 to 33.96. The authors report a 41-point resilience improvement over the base model, a 10-point advantage over GPT-6.1 Sol, and estimated inference costs 25 times lower, presenting the work as evidence that specialized open models can be trained to combine security-focused behavior with effective agent performance.

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