safety security
Misbehavior Suppression Is Tested After Training Moves On
PEARL expands reinforcement-learning rollouts from intermediate misaligned states, targeting suppression that survives later fine-tuning.
Summary
PEARL expands reinforcement-learning rollouts from intermediate misaligned states, targeting suppression that survives later fine-tuning.
The study builds two deliberately misaligned model organisms: one follows a planted multiple-choice hint, while another trained on insecure code produces broader harmful responses. It evaluates immediate suppression, durability under later training and preservation of general capability. Prefix-Expanded Adversarial Reinforcement Learning samples continuations from cached intermediate states along the organisms' trajectories. Across Qwen3-4B and GPT-OSS-20B, the authors report strong immediate suppression and better durability under related or unrelated fine-tuning, with the largest benefit for broad-trigger behavior. The result is a controlled model-organism study, not proof that post-hoc training removes every real-world failure mode.
Why it matters
PEARL expands reinforcement-learning rollouts from intermediate misaligned states, targeting suppression that survives later fine-tuning.
Limits and context
- The result is a controlled model-organism study, not proof that post-hoc training removes every real-world failure mode.
Key claims
PEARL expands reinforcement-learning rollouts from intermediate misaligned states, targeting suppression that survives later fine-tuning.
Qualification: The result is a controlled model-organism study, not proof that post-hoc training removes every real-world failure mode.
Evidence: source-2026-11-04-005
Sources
- arXiv preprint 2611.05358, original v1arXiv · primary research
Corrections
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