Bin Lei

Fork Where the Model Changes Its Mind: Belief-Shift Branching for Tree-Structured Reinforcement Learning

This work formalizes fork placement as locating the pivots of the chain's value curve, where the expected outcome turns, and proposes belief-shift branching, which read the model's answer belief at candidate boundaries and fork just before the step where consecutive beliefs diverge most.