Lead Developer Luo Fuli says MiMo-V2.6 development challenges exceed those of DeepSeek R1.

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According to lead developer Luo Fuli, the development challenges for MiMo-V2.6 now surpass those of DeepSeek R1. She noted that the model employs MixRL and MOPD to address diverse training requirements: MixRL integrates code, agents, vision, and security into a single round, while MOPD isolates complex or subjective tasks. Traders should evaluate support and resistance levels when assessing the risk-to-reward ratio for related investments.
ME AI message, Dongcha Beating AI Brief: Luo Fuli, head of Xiaomi MiMo, further explained this round of reinforcement learning following the release of MiMo-V2.6. She stated that she previously worked on DeepSeek R1, and in her view, the research innovations and engineering challenges behind MiMo-V2.6 surpass those of R1. She also clarified why MiMo uses both MixRL and MOPD. MixRL trains code, general agents, vision, and cybersecurity tasks—all verifiable—simultaneously within a single reinforcement learning loop; MOPD, on the other hand, handles long-duration, hard-to-verify, or subjectively rewarded tasks by training them separately first, then integrating these capabilities back into the main model. Tasks like gaming and 3D require lengthy run times and are difficult to automatically evaluate for correctness. Including them in the same RL loop as code tasks would significantly slow down training. Therefore, MiMo trains these tasks separately and uses MOPD to merge the learned capabilities back into the main model. (Source: BlockBeats)
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