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This work proposes HyperMCTS, a training-free method that augments an ordered MCTS tree with a cross-trajectory hypergraph, allowing outcomes collected under one prefix to inform selection under another while preserving execution histories in the tree.
This work proposes a multi-stage alignment method that teaches models to recall and apply relevant business policies during chain-of-thought reasoning at inference time, without including the full business policy in-context.
This work proposes VEG (verbal ϵ -greedy), a novel framework that leverages external feedback as a dynamic control variable to explicitly balance exploration and exploitation within the semantic space and achieves superior accuracy compared to standard RL baselines.
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