Ensembling Large Language Models with Process Reward-Guided Tree Search
  for Better Complex Reasoning
Despite recent advances in large language models, open-source models often
struggle to consistently perform well on complex reasoning tasks. Existing
ensemble methods, whether applied at the token or output levels, fail to
address these challenges. In response, we present Language model Ensemble with
Monte Carlo Tree Search (LE-MCTS), a novel framework for process-level
ensembling of language models. LE-MCTS formulates step-by-step reasoning with
an ensemble of language models as a Markov decision process. In this framework,
states represent intermediate reasoning paths, while actions consist of
generating the next reasoning step using one of the language models selected
from a predefined pool. Guided by a process-based reward model, LE-MCTS
performs a tree search over the reasoning steps generated by different language
models, identifying the most accurate reasoning chain. Experimental results on
five mathematical reasoning benchmarks demonstrate that our approach
outperforms both single language model decoding algorithms and language model
ensemble methods. Notably, LE-MCTS improves performance by 3.6% and 4.3% on the
MATH and MQA datasets, respectively, highlighting its effectiveness in solving
complex reasoning problems.
676c0c92dd95830fd9d60010
Sungjin Park
676c0c92dd95830fd9d60011
Xiao Liu
claimed_verified
2024-12-30T19:32:19.827Z
63fb6e281b4b1bd4e7ffc5be
https://cdn-avatars.huggingface.co/v1/production/uploads/63fb6e281b4b1bd4e7ffc5be/aiRu_bulgnxvEMrjipGoQ.jpeg
Xiao Liu
user
lx865712528
676c0c92dd95830fd9d60012
Yeyun Gong
676c0c92dd95830fd9d60013
Edward Choi
Ensembling Large Language Models with Process Reward-Guided Tree Search
  for Better Complex Reasoning
Despite recent advances in large language models, open-source models often
struggle to consistently perform well on complex reasoning tasks. Existing
ensemble methods, whether applied at the token or output levels, fail to
address these challenges. In response, we present Language model Ensemble with
Monte Carlo Tree Search (LE-MCTS), a novel framework for process-level
ensembling of language models. LE-MCTS formulates step-by-step reasoning with
an ensemble of language models as a Markov decision process. In this framework,
states represent intermediate reasoning paths, while actions consist of
generating the next reasoning step using one of the language models selected
from a predefined pool. Guided by a process-based reward model, LE-MCTS
performs a tree search over the reasoning steps generated by different language
models, identifying the most accurate reasoning chain. Experimental results on
five mathematical reasoning benchmarks demonstrate that our approach
outperforms both single language model decoding algorithms and language model
ensemble methods. Notably, LE-MCTS improves performance by 3.6% and 4.3% on the
MATH and MQA datasets, respectively, highlighting its effectiveness in solving
complex reasoning problems.
A new ensemble framework combining language models with Monte Carlo Tree Search improves performance on complex mathematical reasoning tasks.
large language models ensemling token-based ensembling output-level ensembling process-level ensembling Markov decision process intermediate reasoning paths reward model Monte Carlo Tree Search LE-MCTS MATH dataset MQA dataset