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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.
Teacher averaging in MOPD, not just raw gradients, dictates student learning, with Adam's momentum and BF16 precision masking significant parameter shifts.
Formal verification can guide LLMs to produce RTL specifications that are significantly more accurate and robust than those generated by LLMs alone.
