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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.
LLM agents are stuck in local adjustment loops, unable to adapt their training strategies despite having the resources to do so.