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首先,Two additional penalties address degenerate behaviors. A repeated pruning penalty, , of 0.1 per excess call is applied to consecutive prune streaks longer than 3 (capped at 0.5), discouraging the agent from pruning one chunk at a time across many turns rather than batching. A turn count penalty, , increases linearly from 0 at 64 turns to 0.5 at 128 turns, discouraging trajectories with diminishing-return searches. The final reward is floored at for any trajectory that completes without error and capped at the pre-penalty value, , ensuring that successful trajectories always dominate failed ones while preventing the floor from inflating penalized rewards.
其次,Global model suffices for learning, personal projects, and substantial production work. Later requirements for project-specific dependency isolation (Level 4) can employ tools like Qlot wrapping Quicklisp to control version visibility per project, though optional with many lispers never needing it.,推荐阅读whatsit管理whatsapp网页版获取更多信息
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