MCTS-RAG: Enhancing Retrieval-Augmented Generation with Monte Carlo Tree
  Search
We introduce MCTS-RAG, a novel approach that enhances the reasoning
capabilities of small language models on knowledge-intensive tasks by
leveraging retrieval-augmented generation (RAG) to provide relevant context and
Monte Carlo Tree Search (MCTS) to refine reasoning paths. MCTS-RAG dynamically
integrates retrieval and reasoning through an iterative decision-making
process. Unlike standard RAG methods, which typically retrieve information
independently from reasoning and thus integrate knowledge suboptimally, or
conventional MCTS reasoning, which depends solely on internal model knowledge
without external facts, MCTS-RAG combines structured reasoning with adaptive
retrieval. This integrated approach enhances decision-making, reduces
hallucinations, and ensures improved factual accuracy and response consistency.
The experimental results on multiple reasoning and knowledge-intensive datasets
datasets (i.e., ComplexWebQA, GPQA, and FoolMeTwice) show that our method
enables small-scale LMs to achieve performance comparable to frontier LLMs like
GPT-4o by effectively scaling inference-time compute, setting a new standard
for reasoning in small-scale models.
67e4c08fd9b7021d4a600fa4
Yunhai Hu
admin_assigned
2025-03-27T09:57:51.052Z
662b4e3bc709a61df840fda1
/avatars/fc73c63a4e1f8fbb084ec43ec9af0af0.svg
Hu Yunhai
user
AlexCCtop
67e4c08fd9b7021d4a600fa5
Yilun Zhao
extracted_confirmed
2025-03-31T03:40:49.669Z
62f662bcc58915315c4eccea
https://cdn-avatars.huggingface.co/v1/production/uploads/62f662bcc58915315c4eccea/zOAQLONfMP88zr70sxHK-.jpeg
Yilun Zhao
user
yilunzhao
67e4c08fd9b7021d4a600fa6
Chen Zhao
admin_assigned
2025-03-27T09:58:04.608Z
660103ec4ae78d4ded4633fc
/avatars/efce106d70f5d092bf44d0638aa49984.svg
CHEN Zhao
user
chenzhao
67e4c08fd9b7021d4a600fa7
Arman Cohan
admin_assigned
2025-03-27T09:57:57.092Z
5f5ba21188f57f65f951f255
https://cdn-avatars.huggingface.co/v1/production/uploads/1599840760465-noauth.png
Arman Cohan
user
armanc
MCTS-RAG: Enhancing Retrieval-Augmented Generation with Monte Carlo Tree
  Search
We introduce MCTS-RAG, a novel approach that enhances the reasoning
capabilities of small language models on knowledge-intensive tasks by
leveraging retrieval-augmented generation (RAG) to provide relevant context and
Monte Carlo Tree Search (MCTS) to refine reasoning paths. MCTS-RAG dynamically
integrates retrieval and reasoning through an iterative decision-making
process. Unlike standard RAG methods, which typically retrieve information
independently from reasoning and thus integrate knowledge suboptimally, or
conventional MCTS reasoning, which depends solely on internal model knowledge
without external facts, MCTS-RAG combines structured reasoning with adaptive
retrieval. This integrated approach enhances decision-making, reduces
hallucinations, and ensures improved factual accuracy and response consistency.
The experimental results on multiple reasoning and knowledge-intensive datasets
datasets (i.e., ComplexWebQA, GPQA, and FoolMeTwice) show that our method
enables small-scale LMs to achieve performance comparable to frontier LLMs like
GPT-4o by effectively scaling inference-time compute, setting a new standard
for reasoning in small-scale models.
MCTS-RAG improves small language models' reasoning by integrating retrieval-augmented generation and Monte Carlo Tree Search, leading to performance comparable to large models on knowledge-intensive tasks.
retrieve-augmented generation MCTS Monte Carlo Tree Search reasoning paths decision-making process ComplexWebQA GPQA FoolMeTwice LMs GPT-4o scaling inference-time compute