Meta 论文提出 RSD，让 AI 智能体从无奖励的历史尝试中学习
Meta 新论文提出 RSD，让同一模型在任务结束后回看每一步并为动作评分，从没有正确答案和奖励的运行中学习。ScienceWorld 上 Qwen3-14B 成功率从 12.9% 升至 20.6%，ALFWorld 上 Qwen3-8B 达到 76.6%，高于测试的其他方法；每轮重建回溯内容时，第 3 轮成功率为 67.9%，固定笔记则为 59.7%。
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.
来源：rohanpaul_ai · x.com