FREESON: Retriever-Free Retrieval-Augmented Reasoning via
  Corpus-Traversing MCTS
Authors: name: Chaeeun Kim, name: Seungone Kim

Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in
multi-step reasoning and calling search engines at appropriate steps. However,
existing retrieval-augmented reasoning approaches rely on separate retrieval
models, limiting the LRM's role in retrieval to deciding when to retrieve and
how to query. This separation not only increases hardware and operational costs
but also leads to errors in the retrieval process due to the representation
bottleneck, a phenomenon where the retriever's embedding space is not
expressive enough to meet the generator's requirements. To address this, we
shift our perspective from sequence-to-sequence matching to locating the
answer-containing paths within the corpus, and propose a novel framework called
FREESON (Retriever-FREE Retrieval-Augmented ReaSONing). This framework enables
LRMs to retrieve relevant knowledge on their own by acting as both a generator
and retriever. To achieve this, we introduce a variant of the MCTS algorithm
specialized for the retrieval task, which we call CT-MCTS (Corpus-Traversing
Monte Carlo Tree Search). In this algorithm, LRMs traverse through the corpus
toward answer-containing regions. Our results on five open-domain QA
benchmarks, including single-hop and multi-hop questions, show that FREESON
achieves an average improvement of 14.4% in EM and F1 over four multi-step
reasoning models with a separate retriever, and it also performs comparably to
the strongest baseline, surpassing it by 3% on PopQA and 2WikiMultihopQA.