Learn to Reason via Mixture-of-Thought
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While most prior approaches train with a single modality and only ensemble during inference, this work introduces Mixture-of-Thought (MoT) to jointly train and infer across modalities, resulting in notable gains in logical reasoning performance. Key findings:
Three-modality synergy: MoT uses natural language for interpretability, code for structured procedural reasoning, and truth tables to explicitly enumerate logical cases. Error analysis shows that truth tables significantly reduce common LLM failure modes like missing branches or invalid converses.
Self-evolving training: MoT introduces an iterative, on-policy training loop where the model generates, filters, and learns from its own multi-modal reasoning traces. This joint training outperforms both single-modality and partial-modality setups.
Inference via voting: At test time, MoT generates predictions from each modality and selects the majority answer, leading to robust predictions. Results show up to +11.7pp average accuracy gains on FOLIO and ProofWriter, with 9B models matching GPT-4 + Logic-LM performance.
Stronger on harder tasks: MoT delivers the largest improvements on problems with higher reasoning depth (5–8 steps). It also shows superior test-time scaling, with more diverse and accurate outputs under fixed inference budgets. MoT demonstrates that LLMs can achieve significantly more robust logical reasoning by reasoning like humans (using multiple modes of thought), not just by sampling more from a single modality.
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