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首先，Reinforcement LearningThe reinforcement learning stage uses a large and diverse prompt distribution spanning mathematics, coding, STEM reasoning, web search, and tool usage across both single-turn and multi-turn environments. Rewards are derived from a combination of verifiable signals, such as correctness checks and execution results, and rubric-based evaluations that assess instruction adherence, formatting, response structure, and overall quality. To maintain an effective learning curriculum, prompts are pre-filtered using open-source models and early checkpoints to remove tasks that are either trivially solvable or consistently unsolved. During training, an adaptive sampling mechanism dynamically allocates rollouts based on an information-gain metric derived from the current pass rate of each prompt. Under a fixed generation budget, rollout allocation is formulated as a knapsack-style optimization, concentrating compute on tasks near the model's capability frontier where learning signal is strongest.
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其次，By starting from scratch we were able to learn from our experience with Vim and make some breaking changes. The result is a much smaller codebase and a modern set of defaults. It's easier to get started if you've never used a modal editor before, and there's much less fiddling with config files.
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第三，34 // the single join block, merging all value results into a single branch
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最后，Moongate uses a strict separation between inbound protocol parsing and outbound event projections:
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