AI论文简报

Arbor科研增益2.5倍，50环境抵300个 (opens in new tab)

Covers Breaking Entropy Bounds: Accelerating RL Training via MTP with Rejection Sampling

Arbor 让 agent 自己跑完整科研循环 ，靠一棵 Hypothesis Tree 把经验跨轮累积，六个真实研究任务全拿最佳，平均相对增益是 Codex 和 Claude Code 的 2.5 倍以上｜AI论文简报

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