Mechanism

Replaces independent flat sampling in the E-step of ReSTEM\text{ReST}^{\text{EM}} with Process-Reward-guided Monte Carlo Tree Search (MCTS⋆MCTS^\star), using inferred step-level values from tree backup to simultaneously train both the policy πθ\pi_\theta and the Process Reward Model QϕQ_\phi in a mutually reinforcing loop.

References

  1. ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Search ↗

    Dan Zhang, Sining Zhoubian, Ziniu Hu, Yisong Yue, Yuxiao Dong, Jie Tang · 2024