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Omni-modal foundation models achieve self-improvement via RL compute scaled to 1M context, leveraging asynchronous training and agentic grading for enhanced intelligence.
Multimodal unlearning could revolutionize how we handle sensitive data in AI, enabling targeted removal without sacrificing model performance.
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.
