Unverified50% confidenceSolutionExact time
During the mid-training phase, ZGCM-1 reformulates the agent-tool interaction process as a Markov Decision Process (MDP).
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9/15/2026
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UnverifiedThe core proposition of ZGCM-1 is that compact models can break through parameter capacity limitations by coupling deliberate internal thinking with active external tool invocation.75% similarUnverified马尔可夫决策过程（MDP）为智能体的感知—决策—执行闭环提供了数学框架69% similarVerifiedThe core framework of reinforcement learning consists of five elements—agent, environment, state, action, and reward—built on the Markov Decision Process (MDP) and describing decision problems with the five-tuple (S, A, P, R, γ)68% similarUnverifiedCompared with traditional model predictive control (MPC) or ZMP methods, reinforcement learning policies require no explicit modeling of dynamic equations and can automatically discover efficient motion patterns through massive trial and error.67% similarVerifiedReinforcement learning learns optimal policies through continuous interaction between an agent and its environment; its mathematical foundation is the Markov Decision Process (MDP), which consists of a state space, action space, transition function, reward function, and discount factor.67% similar
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