OpenNovelty.org

Toward Effective Tool-Integrated Reasoning via Self-Evolved Preference Learning

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
reasoning modeltool-integrated reasoningself-evolved traininginformation entropy
Abstract:

Tool-Integrated Reasoning (TIR) enables large language models (LLMs) to enhance their internal reasoning ability by integrating external tools. However, models with TIR often exhibit suboptimal behaviors, including insufficient tool calls, excessive tool calls, and overthinking after receiving tool call results. How to empower LLMs to perform TIR efficiently and accurately, while stabilizing the reasoning process, remains an open challenge. In this paper, we first analyze the impact of tool calls on model reasoning from the perspective of information entropy. We find that when tool call results are provided, the information entropy of subsequent reasoning content will show a clear trend of change, and the overall information entropy of the reasoning chain will vary depending on the number of tool calls. Based on these observations, we propose Tool-Light, a framework designed to encourage LLMs to perform TIR efficiently and accurately. Our framework consists of dataset construction and multi-stage fine-tuning. For dataset construction, we use the trained model for continuous self-evolved sampling, integrating two methods: vanilla sampling and entropy-guided sampling. At the same time, during the sampling process, we design strict criteria for selecting positive-negative pairs. For the training process, we introduce a two-stage method, which includes a Supervised Fine-Tuning (SFT), and Self-Evolved Direct Preference Optimization (DPO). Test results on 10 datasets reveal the effectiveness of Tool-Light, significantly improving the efficiency and accuracy of the model in completing TIR tasks.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper introduces Tool-Light, a framework that applies information entropy analysis to guide tool-integrated reasoning in large language models. It sits within the Self-Improvement and Iterative Refinement leaf of the taxonomy, which contains four papers total. This leaf focuses on methods where models iteratively generate training data and refine tool-use strategies through repeated sampling. The research direction is moderately populated, representing one of four training-focused subtopics in a taxonomy of fifty papers across the broader field of tool-integrated reasoning.

The taxonomy reveals that Tool-Light's leaf neighbors include Reinforcement Learning for Tool Use (six papers), Supervised Fine-Tuning (two papers), and Preference Learning (one paper). The framework's entropy-guided sampling connects conceptually to preference-based optimization methods, while its multi-stage fine-tuning bridges toward supervised approaches. The taxonomy's scope note explicitly distinguishes self-improvement methods from single-pass supervised training and static RL, positioning Tool-Light at the intersection of iterative refinement and preference-driven optimization within the training methods branch.

Among twenty-three candidates examined, the entropy-based analysis contribution shows overlap with two prior works from ten candidates reviewed, while the entropy-guided sampling strategy appears refuted by one of three candidates examined. The Tool-Light framework itself shows no clear refutation across ten candidates. The limited search scope means these statistics reflect top-K semantic matches rather than exhaustive coverage. The entropy analysis and sampling contributions face more substantial prior work, whereas the integrated framework appears more distinctive within the examined candidate set.

Based on the limited literature search, the work demonstrates moderate novelty in its integrated approach, though individual components show varying degrees of prior coverage. The analysis captures top-ranked semantic matches and does not claim comprehensive field coverage. The framework's positioning within a moderately populated taxonomy leaf suggests it contributes to an active but not overcrowded research direction.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
23
Contribution Candidate Papers Compared
3
Refutable Paper

Research Landscape Overview

Core task: Tool-integrated reasoning with large language models. The field has coalesced around several major branches that reflect different facets of enabling LLMs to leverage external tools effectively. Frameworks and Architectures for Tool Integration establish the foundational systems and interfaces—ranging from early self-supervised approaches like Toolformer[10] to comprehensive platforms such as ToolLLM[20] and Chameleon[16]—that allow models to discover, select, and invoke diverse tools. Training and Optimization Methods focus on how models learn to use tools, encompassing self-improvement techniques (e.g., Self-Training Tool-Use[34]), iterative refinement strategies, and specialized training regimes like those in ToRA[4] and Qwen Math[27]. Domain-Specific Tool-Integrated Applications demonstrate the breadth of practical deployment, from scientific discovery (SciAgent[47], Chemistry Tools[18]) to healthcare (EHRAgent[22]) and mathematical problem-solving (Multi-tool Math[15]). Reasoning Paradigms and Cognitive Mechanisms explore the underlying decision-making processes, including meta-reasoning[6] and computational thinking[7], while Evaluation and Analysis branches address benchmarking (ToolQA[8], Metatool Benchmark[14]) and challenges such as tool-induced hallucinations[17]. Within the Training and Optimization Methods branch, a particularly active line of work centers on self-improvement and iterative refinement, where models bootstrap their own tool-use capabilities through feedback loops and preference learning. Self-Evolved Preference[0] exemplifies this direction by developing mechanisms for models to refine their tool-calling strategies based on self-generated preferences, closely aligning with neighboring efforts like Self-Training Tool-Use[34] that similarly leverage iterative cycles to enhance performance without extensive human annotation. In contrast, Qwen Math[27] emphasizes domain-targeted training with curated mathematical tool integration, illustrating a trade-off between general self-improvement and task-specific optimization. These self-improvement approaches raise open questions about scalability, the quality of self-generated signals, and how to balance exploration of novel tool combinations with exploitation of known effective strategies, themes that resonate across the broader landscape of tool-integrated reasoning.

Claimed Contributions

Entropy-based analysis of Tool-Integrated Reasoning

The authors analyze Tool-Integrated Reasoning tasks using information entropy metrics, revealing that tool call results cause predictable entropy fluctuations and that reasoning paths with fewer tool calls tend to exhibit lower overall entropy distributions.

10 retrieved papers
Can Refute
Entropy-guided sampling strategy combined with two-stage training

The authors introduce an entropy-guided sampling method that branches from high-entropy positions to generate diverse reasoning paths, integrated with a two-stage training pipeline consisting of supervised fine-tuning followed by self-evolved direct preference optimization.

3 retrieved papers
Can Refute
Tool-Light framework for effective Tool-Integrated Reasoning

The authors develop Tool-Light, a comprehensive framework that combines dataset construction through vanilla and entropy-guided sampling with multi-stage fine-tuning to improve both the efficiency and accuracy of tool calls in reasoning tasks.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Entropy-based analysis of Tool-Integrated Reasoning

The authors analyze Tool-Integrated Reasoning tasks using information entropy metrics, revealing that tool call results cause predictable entropy fluctuations and that reasoning paths with fewer tool calls tend to exhibit lower overall entropy distributions.

Contribution

Entropy-guided sampling strategy combined with two-stage training

The authors introduce an entropy-guided sampling method that branches from high-entropy positions to generate diverse reasoning paths, integrated with a two-stage training pipeline consisting of supervised fine-tuning followed by self-evolved direct preference optimization.

Contribution

Tool-Light framework for effective Tool-Integrated Reasoning

The authors develop Tool-Light, a comprehensive framework that combines dataset construction through vanilla and entropy-guided sampling with multi-stage fine-tuning to improve both the efficiency and accuracy of tool calls in reasoning tasks.

Table of Contents