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Meta published a paper proposing the RSD method, which lets AI agents, after knowing the task outcome, review each step and learn from historical attempts that no one labeled as right or wrong. Experimental results reported in the paper show: on ScienceWorld, Qwen3-14B's success rate rose from 12.9% to 20.6%; on ALFWorld, Qwen3-8B reached 76.6%, surpassing all tested methods, including those that received rewards or standard answers. The authors also recommend retaining agents' unlabeled run records (including failures) and regenerating the review notes each round, because when notes were frozen at round 3, ALFWorld success rate stalled at 59.7%, whereas with notes updated each round it was 67.9%. Currently this work is released as a paper, with no subsequent progress yet.
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Oct 23
Rohan Paul
Meta's new paper proposes RSD: agents can learn from unlabeled historical trajectories
Meta's new paper proposes the RSD method, which lets AI agents review each step after knowing the task outcome and learn from historical attempts that no one labeled as right or wrong. On ScienceWorld, Qwen3-14B's success rate rose from 12.9% to 20.6%; on ALFWorld, Qwen3-8B reached 76.6%, surpassing all tested methods, including those that received rewards or standard answers. The authors recommend keeping agents' unlabeled run records (including failures) and regenerating the review notes each round, because when notes were frozen at round 3 the ALFWorld success rate stayed at 59.7%, while updating notes each round gave 67.9%.