Scaling Reasoning can Improve Factuality in Large Language Models
Authors: name: Mike Zhang, name: Johannes Bjerva, name: Russa Biswas

Recent studies on large language model (LLM) reasoning capabilities have
demonstrated promising improvements in model performance by leveraging a
lengthy thinking process and additional computational resources during
inference, primarily in tasks involving mathematical reasoning (Muennighoff et
al., 2025). However, it remains uncertain if longer reasoning chains inherently
enhance factual accuracy, particularly beyond mathematical contexts. In this
work, we thoroughly examine LLM reasoning within complex open-domain
question-answering (QA) scenarios. We initially distill reasoning traces from
advanced, large-scale reasoning models (QwQ-32B and DeepSeek-R1-671B), then
fine-tune a variety of models ranging from smaller, instruction-tuned variants
to larger architectures based on Qwen2.5. To enrich reasoning traces, we
introduce factual information from knowledge graphs in the form of paths into
our reasoning traces. Our experimental setup includes four baseline approaches
and six different instruction-tuned models evaluated across a benchmark of six
datasets, encompassing over 22.6K questions. Overall, we carry out 168
experimental runs and analyze approximately 1.7 million reasoning traces. Our
findings indicate that, within a single run, smaller reasoning models achieve
noticeable improvements in factual accuracy compared to their original
instruction-tuned counterparts. Moreover, our analysis demonstrates that adding
test-time compute and token budgets factual accuracy consistently improves by
2-8%, further confirming the effectiveness of test-time scaling for enhancing
performance and consequently improving reasoning accuracy in open-domain QA
tasks. We release all the experimental artifacts for further research.