The authors ran controlled reinforcement-learning experiments on ALFWorld and ScienceWorld.
Accurate World Predictions Are Not Required for Agent Training Gains
Controlled experiments found that mismatched observations and random rewards could improve agents despite providing no correct information about environment transitions.
Research Lab
Xinyu Che · Hang Yan · Yanchen Liu · Haochen Liu · Ruifeng Li · Anran Shi · +2 more
Xi’an Jiaotong University · University of Southern California · University of the Chinese Academy of Sciences · East China Normal University
Research Digest··2 min read
Che et al.
Why this paper
From University of the Chinese Academy of Sciences and 3 others
In one line
World model post-training improves agent performance through training effects other than accurate world prediction, as mismatched targets retain task gains despite lower accuracy.
What we could check
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- ✓Reports numbers on named benchmarks
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