Agentic Transformers Provably Learn to Search via Reinforcement Learning

arXiv:2606.00183v1 Announce Type: new Abstract: Tree search is a central abstraction behind many language-agent reasoning and decision-making tasks: agents must explore actions, remember failures, and backtrack toward promising alternatives. Yet, we lack a theoretical understanding of how transformer-based policies acquire such search capabilities from the training dynamics of reinforcement learning (RL). We study this question in a stochastic $k$-ary tree environment, where an agentic transformer observes only its trajectory history through interaction and receives a terminal reward for reach

Source: arXiv cs.LG — read the full report at the original publisher.

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