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MOPD achieves superior capability integration in LLMs by distilling knowledge from multiple RL teachers without losing performance, setting a new standard for post-training methods.
Current autonomous agent benchmarks miss nearly half of safety violations and over 10% of robustness failures because they only check final outputs, a problem Claw-Eval directly addresses.