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Hong Kong University of Science and Technology
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Learning algorithms might excel in memorization but can falter in broader generalization, with RL outperforming SFT in transferring knowledge across contexts.
MiniMax-M2 proves that massive parameter counts don't always translate to better agentic performance; strategic activation of a smaller subset can unlock frontier-level intelligence.
An agentic framework, Dr.~RTL, outperforms industry-leading commercial tools in RTL timing optimization by 21% WNS and 17% TNS improvement, demonstrating the potential of tool-grounded self-improvement.
