Enhancing Logical Reasoning in Language Models via Symbolically-Guided Monte Carlo Process Supervision

• URL: http://arxiv.org/abs/2505.20415v2
• Date: Wed, 17 Sep 2025 21:32:35 GMT
• Title: Enhancing Logical Reasoning in Language Models via Symbolically-Guided Monte Carlo Process Supervision
• Authors: Xingwei Tan, Marco Valentino, Mahmud Akhter, Maria Liakata, Nikolaos Aletras,
• Abstract summary: Large language models (LLMs) have shown strong performance in many reasoning benchmarks.<n>LLMs are susceptible to content variations, demonstrating a lack of robust planning or symbolic abstractions.<n>We propose to overcome such limitations by synthesizing high-quality symbolic reasoning trajectories with stepwise pseudo-labels.
• Score: 43.05159920832912
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large language models (LLMs) have shown strong performance in many reasoning benchmarks. However, recent studies have pointed to memorization, rather than generalization, as one of the leading causes for such performance. LLMs, in fact, are susceptible to content variations, demonstrating a lack of robust planning or symbolic abstractions supporting their reasoning process. To improve reliability, many attempts have been made to combine LLMs with symbolic methods. Nevertheless, existing approaches fail to effectively leverage symbolic representations due to the challenges involved in developing reliable and scalable verification mechanisms. In this paper, we propose to overcome such limitations by synthesizing high-quality symbolic reasoning trajectories with stepwise pseudo-labels at scale via Monte Carlo estimation. A Process Reward Model (PRM) can be efficiently trained based on the synthesized data and then used to select more symbolic trajectories. The trajectories are then employed with Direct Preference Optimization (DPO) and Supervised Fine-Tuning (SFT) to improve logical reasoning and generalization. Our results on benchmarks (i.e., FOLIO and LogicAsker) show the effectiveness of the proposed method with gains on frontier and open-weight models. Moreover, additional experiments on claim verification data reveal that fine-tuning on the generated symbolic reasoning trajectories enhances out-of-domain generalizability, suggesting the potential impact of the proposed method in enhancing planning and logical reasoning.

Related papers

• Pushing LLMs to Their Logical Reasoning Bound: The Role of Data Reasoning Intensity [59.27594125465172]
  We introduce Data Reasoning Intensity (DRI), a novel metric that quantifies the latent logical reasoning complexity of samples.<n>We then introduce a re-cognizing optimization strategy that systematically enhances the logical reasoning intensity of training data.
  arXiv  Detail & Related papers  (2025-09-29T14:20:04Z)
• Revisiting LLM Reasoning via Information Bottleneck [57.519119962528166]
  Large language models (LLMs) have recently demonstrated remarkable progress in reasoning capabilities through reinforcement learning with verifiable rewards (RLVR)<n>We present a theoretical characterization of LLM reasoning grounded in information bottleneck (IB) principle.<n>We propose IB-aware reasoning optimization (IBRO), a framework that encourages reasoning trajectories to be both informative about the final correct answer and generalizable.
  arXiv  Detail & Related papers  (2025-07-24T13:14:25Z)
• CTRLS: Chain-of-Thought Reasoning via Latent State-Transition [57.51370433303236]
  Chain-of-thought (CoT) reasoning enables large language models to break down complex problems into interpretable intermediate steps.<n>We introduce groundingS, a framework that formulates CoT reasoning as a Markov decision process (MDP) with latent state transitions.<n>We show improvements in reasoning accuracy, diversity, and exploration efficiency across benchmark reasoning tasks.
  arXiv  Detail & Related papers  (2025-07-10T21:32:18Z)
• Beyond Templates: Dynamic Adaptation of Reasoning Demonstrations via Feasibility-Aware Exploration [15.711365331854614]
  We introduce Dynamic Adaptation of Reasoning Trajectories (DART), a novel data adaptation framework.<n>Instead of uniformly imitating expert steps, DART employs a selective imitation strategy guided by step-wise adaptability estimation.<n>We validate DART across multiple reasoning benchmarks and model scales, demonstrating that it significantly improves generalization and data efficiency.
  arXiv  Detail & Related papers  (2025-05-27T04:08:11Z)
• BRiTE: Bootstrapping Reinforced Thinking Process to Enhance Language Model Reasoning [78.63421517563056]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks.<n>We present a unified probabilistic framework that formalizes LLM reasoning through a novel graphical model.<n>We introduce the Bootstrapping Reinforced Thinking Process (BRiTE) algorithm, which works in two steps.
  arXiv  Detail & Related papers  (2025-01-31T02:39:07Z)
• Reversal of Thought: Enhancing Large Language Models with Preference-Guided Reverse Reasoning Warm-up [9.42385235462794]
  Large language models (LLMs) have shown remarkable performance in reasoning tasks but face limitations in mathematical and complex logical reasoning.<n>We propose Reversal of Thought (RoT) to enhance the logical reasoning abilities of LLMs during the warm-up phase prior to batch inference.<n>RoT utilizes a Preference-Guided Reverse Reasoning warm-up strategy, which integrates logical symbols for pseudocode planning.
  arXiv  Detail & Related papers  (2024-10-16T07:44:28Z)
• Enhancing Logical Reasoning in Large Language Models through Graph-based Synthetic Data [53.433309883370974]
  This work explores the potential and limitations of using graph-based synthetic reasoning data as training signals to enhance Large Language Models' reasoning capabilities.<n>Our experiments, conducted on two established natural language reasoning tasks, demonstrate that supervised fine-tuning with synthetic graph-based reasoning data effectively enhances LLMs' reasoning performance without compromising their effectiveness on other standard evaluation benchmarks.
  arXiv  Detail & Related papers  (2024-09-19T03:39:09Z)
• Thought-Like-Pro: Enhancing Reasoning of Large Language Models through Self-Driven Prolog-based Chain-of-Thought [31.964412924094656]
  Large language models (LLMs) have shown exceptional performance as general-purpose assistants. We introduce a novel learning framework, THOUGHT-LIKE-PRO, to facilitate learning and generalization across diverse reasoning tasks. Our empirical findings indicate that our proposed approach substantially enhances the reasoning abilities of LLMs.
  arXiv  Detail & Related papers  (2024-07-18T18:52:10Z)
• LLMs for Relational Reasoning: How Far are We? [8.840750655261251]
  Large language models (LLMs) have revolutionized many areas by achieving state-of-the-art performance on downstream tasks. Recent efforts have demonstrated that the LLMs are poor at solving sequential decision-making problems.
  arXiv  Detail & Related papers  (2024-01-17T08:22:52Z)
• Towards LogiGLUE: A Brief Survey and A Benchmark for Analyzing Logical Reasoning Capabilities of Language Models [56.34029644009297]
  Large language models (LLMs) have demonstrated the ability to overcome various limitations of formal Knowledge Representation (KR) systems. LLMs excel most in abductive reasoning, followed by deductive reasoning, while they are least effective at inductive reasoning. We study single-task training, multi-task training, and "chain-of-thought" knowledge distillation fine-tuning technique to assess the performance of model.
  arXiv  Detail & Related papers  (2023-10-02T01:00:50Z)
• Exploring Self-supervised Logic-enhanced Training for Large Language Models [59.227222647741094]
  In this paper, we make the first attempt to investigate the feasibility of incorporating logical knowledge through self-supervised post-training. We devise an auto-regressive objective variant of MERIt and integrate it with two LLM series, i.e., FLAN-T5 and LLaMA, with parameter size ranging from 3 billion to 13 billion. The results on two challenging logical reasoning benchmarks demonstrate the effectiveness of LogicLLM.
  arXiv  Detail & Related papers  (2023-05-23T06:13:10Z)
• Improved Logical Reasoning of Language Models via Differentiable Symbolic Programming [12.984852480664378]
  Pre-trained large language models (LMs) struggle to perform logical reasoning reliably despite advances in scale and compositionality. We propose DSR-LM, a Differentiable Symbolic Reasoning framework where pre-trained LMs govern the perception of factual knowledge, and a symbolic module performs deductive reasoning.
  arXiv  Detail & Related papers  (2023-05-05T07:24:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.