Meta 新论文提出 RSD：智能体可从无标注历史轨迹中学习
New Meta paper finds that AI agents can learn from their own past attempts at tasks, even ones nobody marked right or wrong, by looking back at each step once t
Meta 新论文提出 RSD 方法，让 AI 智能体在知道任务结局后回看每一步，从没人标注对错的历史尝试中学习。在 ScienceWorld 上，Qwen3-14B 的成功率从 12.9% 升到 20.6%；在 ALFWorld 上，Qwen3-8B 达到 76.6%，超过所有被测方法，包括那些获得奖励或标准答案的方法。作者建议保留智能体未标注的运行记录（包括失败），每轮重新生成回看笔记，因为冻结笔记到第 3 轮时 ALFWorld 成功率停在 59.7%，而每轮更新笔记时为 67.9%。
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Meta 的 RSD 方法让智能体从无标注的完整轨迹中学习，ScienceWorld 与 ALFWorld 上的成功率数字给出了可对照的效果。
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New Meta paper finds that AI agents can learn from their own past attempts at tasks, even ones nobody marked right or wrong, by looking back at each step once they know how it ended.
Most agent runs come with no correct answer and no reward, which standard training relies on. RSD has the same model reread each finished run, knowing how it ended and what worked on similar tasks. It scores every action, and the agent learns from those scores.
On ScienceWorld, Qwen3-14B's success rate rose from 12.9% to 20.6%. On ALFWorld, Qwen3-8B hit 76.6%, beating every other method tested, even ones given rewards or ground truth.
Keep your agent's unlabeled runs, failures included, and train it on a hindsight review of them. Rebuild the review each round, since frozen notes left ALFWorld success at 59.7% by round 3, versus 67.9% with fresh ones.