From to : Multidimensional Supervision of Reasoning Process for LLM Optimization

• URL: http://arxiv.org/abs/2510.11457v1
• Date: Mon, 13 Oct 2025 14:29:15 GMT
• Title: From <Answer> to <Think>: Multidimensional Supervision of Reasoning Process for LLM Optimization
• Authors: Beining Wang, Weihang Su, Hongtao Tian, Tao Yang, Yujia Zhou, Ting Yao, Qingyao Ai, Yiqun Liu,
• Abstract summary: Dimension-level Reward Model (DRM) is a new supervision framework for Large Language Models.<n>DRM evaluates the quality of a reasoning process along three fundamental, complementary, and interpretable dimensions.<n> Experimental results show that DRM provides effective supervision signals, guides the optimization of LLMs and enhances their reasoning ability.
• Score: 62.07990937720985
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Improving the multi-step reasoning ability of Large Language Models (LLMs) is a critical yet challenging task. The dominant paradigm, outcome-supervised reinforcement learning (RLVR), rewards only correct final answers, often propagating flawed reasoning and suffering from sparse reward signals. While process-level reward models (PRMs) provide denser, step-by-step feedback, they lack generalizability and interpretability, requiring task-specific segmentation of the reasoning process. To this end, we propose the Dimension-level Reward Model (DRM), a new supervision framework that bridges the gap between these two approaches. DRM evaluates the quality of a reasoning process along three fundamental, complementary, and interpretable dimensions: Confidence for uncertainty calibration, Relevance for semantic alignment, and Coherence for logical consistency. Together, these dimensions capture aspects beyond final answer correctness and enable interpretable assessment without requiring ground truth answers. Experimental results show that DRM provides effective supervision signals, guides the optimization of LLMs and enhances their reasoning ability. In particular, DRM-supervised training achieves consistent gains on both in-distribution and out-of-distribution open-domain tasks, including mathematics, question answering, code execution, and puzzles. Our findings demonstrate that multidimensional supervision of the reasoning process can improve the generalized reasoning ability of LLMs beyond the training distribution.

Related papers

• Balancing Faithfulness and Performance in Reasoning via Multi-Listener Soft Execution [79.98699884805636]
  Reasoning Execution by Multiple Listeners (REMUL) is a multi-party reinforcement learning approach.<n>REMUL builds on the hypothesis that reasoning traces which other parties can follow will be more faithful.<n>Speakers are rewarded for producing reasoning that is clear to listeners.
  arXiv  Detail & Related papers  (2026-02-18T02:55:55Z)
• Towards Generalizable Reasoning: Group Causal Counterfactual Policy Optimization for LLM Reasoning [50.352417879912515]
  Large language models (LLMs) excel at complex tasks with advances in reasoning capabilities.<n>We propose Group Causal Counterfactual Policy Optimization to explicitly train LLMs to learn generalizable reasoning patterns.<n>We then construct token-level advantages from this reward and optimize the policy, encouraging LLMs to favor reasoning patterns that are process-valid and counterfactually robust.
  arXiv  Detail & Related papers  (2026-02-06T08:03:11Z)
• An Empirical Study of Reasoning Steps in Thinking Code LLMs [8.653365851909745]
  Thinking Large Language Models generate explicit intermediate reasoning traces before final answers.<n>This study examines the reasoning process and quality of thinking LLMs for code generation.
  arXiv  Detail & Related papers  (2025-11-08T06:18:48Z)
• ReaLM: Reflection-Enhanced Autonomous Reasoning with Small Language Models [76.28894983518164]
  Small Language Models (SLMs) are a cost-effective alternative to Large Language Models (LLMs)<n>They often struggle with complex reasoning due to their limited capacity and a tendency to produce mistakes or inconsistent answers.<n>We introduce ReaLM, a reinforcement learning framework for robust and self-sufficient reasoning in vertical domains.
  arXiv  Detail & Related papers  (2025-08-17T14:50:23Z)
• Coherent Multimodal Reasoning with Iterative Self-Evaluation for Vision-Language Models [4.064135211977999]
  Large language models (LLMs) and vision-language models (LVLMs) struggle with complex, multi-step, cross-modal common sense reasoning tasks.<n>We propose the Coherent Multimodal Reasoning Framework (CMRF), a novel approach that enhances LVLMs' common sense reasoning capabilities.<n>CMRF mimics human problem-solving by decomposing complex queries, generating step-by-step inferences, and self-correcting errors.
  arXiv  Detail & Related papers  (2025-08-04T20:33:58Z)
• Learning Deliberately, Acting Intuitively: Unlocking Test-Time Reasoning in Multimodal LLMs [7.501387372794562]
  Deliberate-to-Intuitive reasoning framework (D2I) improves understanding and reasoning ability of multimodal language models.<n>Our method sets deliberate reasoning strategies to enhance modality alignment only through the rule-based format reward during training.<n>While evaluating, the reasoning style shifts to intuitive, which removes deliberate reasoning strategies during training and implicitly reflects the model's acquired abilities in the response.
  arXiv  Detail & Related papers  (2025-07-09T16:25:44Z)
• MPBench: A Comprehensive Multimodal Reasoning Benchmark for Process Errors Identification [27.594868471770475]
  Reasoning is an essential capacity for large language models (LLMs) to address complex tasks.<n>Process-level reward models (PRMs) were proposed to provide step-wise rewards that facilitate reinforcement learning and data production.<n>Existing benchmarks of PRMs are text-based and focus on error detection, neglecting other scenarios like reasoning search.<n>MPBench is a comprehensive, multi-task, multimodal benchmark designed to systematically assess the effectiveness of PRMs in diverse scenarios.
  arXiv  Detail & Related papers  (2025-03-16T13:50:38Z)
• ReMA: Learning to Meta-think for LLMs with Multi-Agent Reinforcement Learning [53.817538122688944]
  We introduce Reinforced Meta-thinking Agents (ReMA) to elicit meta-thinking behaviors from Reasoning of Large Language Models (LLMs)<n>ReMA decouples the reasoning process into two hierarchical agents: a high-level meta-thinking agent responsible for generating strategic oversight and plans, and a low-level reasoning agent for detailed executions.<n> Empirical results from single-turn experiments demonstrate that ReMA outperforms single-agent RL baselines on complex reasoning tasks.
  arXiv  Detail & Related papers  (2025-03-12T16:05:31Z)
• Reinforcing Thinking through Reasoning-Enhanced Reward Models [6.636512424910708]
  Large Language Models (LLMs) exhibit great potential in complex multi-step reasoning through inference-time thinking.<n>LLMs struggle with deciding when to stop thinking due to limited self-awareness about their knowledge boundaries.<n>This work addresses these challenges by distilling the LLM's own reasoning processes into synthetic behavioral data.
  arXiv  Detail & Related papers  (2024-12-31T04:50:15Z)
• Improving LLM Reasoning through Scaling Inference Computation with Collaborative Verification [52.095460362197336]
  Large language models (LLMs) struggle with consistent and accurate reasoning. LLMs are trained primarily on correct solutions, reducing their ability to detect and learn from errors. We propose a novel collaborative method integrating Chain-of-Thought (CoT) and Program-of-Thought (PoT) solutions for verification.
  arXiv  Detail & Related papers  (2024-10-05T05:21:48Z)
• MR-Ben: A Meta-Reasoning Benchmark for Evaluating System-2 Thinking in LLMs [55.20845457594977]
  Large language models (LLMs) have shown increasing capability in problem-solving and decision-making.<n>We present a process-based benchmark MR-Ben that demands a meta-reasoning skill.<n>Our meta-reasoning paradigm is especially suited for system-2 slow thinking.
  arXiv  Detail & Related papers  (2024-06-20T03:50:23Z)

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.