Learning to Reason via Mixture-of-Thought for Logical Reasoning
Authors: name: Tong Zheng, name: Lichang Chen, name: Simeng Han, name: R. Thomas McCoy, name: Heng Huang

Human beings naturally utilize multiple reasoning modalities to learn and
solve logical problems, i.e., different representational formats such as
natural language, code, and symbolic logic. In contrast, most existing
LLM-based approaches operate with a single reasoning modality during training,
typically natural language. Although some methods explored modality selection
or augmentation at inference time, the training process remains modality-blind,
limiting synergy among modalities. To fill in this gap, we propose
Mixture-of-Thought (MoT), a framework that enables LLMs to reason across three
complementary modalities: natural language, code, and a newly introduced
symbolic modality, truth-table, which systematically enumerates logical cases
and partially mitigates key failure modes in natural language reasoning. MoT
adopts a two-phase design: (1) self-evolving MoT training, which jointly learns
from filtered, self-generated rationales across modalities; and (2) MoT
inference, which fully leverages the synergy of three modalities to produce
better predictions. Experiments on logical reasoning benchmarks including FOLIO
and ProofWriter demonstrate that our MoT framework consistently and
significantly outperforms strong LLM baselines with single-modality
chain-of-thought approaches, achieving up to +11.7pp average accuracy gain.
Further analyses show that our MoT framework benefits both training and
inference stages; that it is particularly effective on harder logical reasoning
problems; and that different modalities contribute complementary strengths,
with truth-table reasoning helping to overcome key bottlenecks in natural
language inference.