Pith. sign in

REVIEW 7 cited by

Learning to Reason via Mixture-of-Thought for Logical Reasoning

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2505.15817 v2 pith:RJVQASIT submitted 2025-05-21 cs.CL

Learning to Reason via Mixture-of-Thought for Logical Reasoning

Tong Zheng , Lichang Chen , Simeng Han , R. Thomas McCoy , Heng Huang This is my paper
classification cs.CL
keywords reasoningmodalitieslanguagelogicalnaturalinferencetrainingframework
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
0 comments
Share X Bluesky LinkedIn Reddit HN
read the original abstract

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.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Small RL Controller, Large Language Model: RL-Guided Adaptive Sampling for Test-Time Scaling

    cs.CL 2026-06 unverdicted novelty 6.0

    RL-trained lightweight controller using answer statistics improves trade-offs among correctness, latency, and total samples in adaptive sampling for LLM test-time scaling.

  2. Too Correct to Learn: Reinforcement Learning on Saturated Reasoning Data

    cs.LG 2026-04 unverdicted novelty 6.0

    A parameter-free sampling strategy called CUTS combined with Mixed-CUTS training prevents mode collapse in RL for saturated LLM reasoning tasks and raises AIME25 Pass@1 accuracy by up to 15.1% over standard GRPO.

  3. Large Lemma Miners: Can LLMs do Induction Proofs for Hardware?

    cs.LO 2025-11 conditional novelty 6.0

    A neurosymbolic method using two LLM prompting frameworks generates provably correct inductive arguments for 84% of a set of mid-size open-source RTL hardware designs.

  4. Parallel-R1: Towards Parallel Thinking via Reinforcement Learning

    cs.CL 2025-09 conditional novelty 6.0

    Parallel-R1 uses SFT cold-start on easy math plus GRPO on hard math to instill parallel thinking in Qwen3-4B, reporting 8.4% average accuracy gains and a 42.9% AIME25 gain from a parallel-exploration scaffold.

  5. VeriTrans: Fine-Tuned LLM-Assisted NL-to-PL Translation via a Deterministic Neuro-Symbolic Pipeline

    cs.AI 2026-04 unverdicted novelty 5.0

    VeriTrans achieves 94.46% SAT/UNSAT correctness on SatBench via LLM translation gated by round-trip similarity and deterministic neuro-symbolic execution.

  6. Large Lemma Miners: Can LLMs do Induction Proofs for Hardware?

    cs.LO 2025-11 conditional novelty 5.0

    LLMs, verified by a symbolic model checker, produced correct inductive strengthenings for 82 of 94 curated RTL safety properties.

  7. CDE: Curiosity-Driven Exploration for Efficient Reinforcement Learning in Large Language Models

    cs.CL 2025-09 conditional novelty 5.0

    Adding actor perplexity and multi-head critic variance as intrinsic exploration bonuses improves RLVR math reasoning accuracy by roughly +2 to +3 points on AIME benchmarks.