Yingbin Liang
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126
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ML×1AI×1Optimization and Control×1Stats ML×1
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2026
Agentic Transformers Provably Learn to Search via Reinforcement Learning
This paper demonstrates that transformer-based policies can provably learn complex tree search mechanisms, such as depth-first search, purely through reinforcement learning in a stochastic environment.
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