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ResT: Reshaping Token-Level Policy Gradients for Tool-Use Large Language Models

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Token-level Policy Gradients Reshape;Tool-use Large Language Model; Entropy-aware; Reinforcement Learning; Reasoning Model
Abstract:

Large language models (LLMs) transcend passive generation and act as goal-directed agents by invoking external tools. Reinforcement learning (RL) offers a principled framework for optimizing these emergent tool-use policies, yet the prevailing paradigm relies exclusively on sparse outcome rewards and lacks consideration of the particularity of tool-use tasks, inflating policy-gradient variance and resulting in inefficient training. To better understand and address these challenges, we first establish a theoretical link between policy entropy and training stability of tool-use tasks, which reveals that structured, low-entropy tokens are primary determinants of rewards. Motivated by this insight, we propose Reshaped Token-level policy gradients (ResT) for tool-use tasks. ResT reshapes the policy gradient through entropy-informed token reweighting, progressively upweighting reasoning tokens as training proceeds. This scheme enables a smooth shift from structural correctness to semantic reasoning and stabilizes convergence in multi-turn tool-use tasks. Evaluation on BFCL and API-Bank shows that ResT outperforms other strong baselines, outperforming prior methods by up to 8.76%. When fine-tuned on a 4B base LLM, ResT further surpasses GPT-4o by 4.11% on single-turn tasks and 1.50% on multi-turn base tasks. Code is available at https://anonymous.4open.science/r/ResT_Tool_use_LLM-F11B.

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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 proposes ResT, a method for optimizing multi-turn tool-use policies in LLMs through entropy-informed token-level gradient reshaping. It resides in the 'Multi-Turn Tool-Use Policy Optimization' leaf, which contains six papers including the original work. This leaf sits within the broader 'Multi-Turn Agentic Tool-Use and Workflow Optimization' branch, one of the most populated areas in the taxonomy with five distinct sub-categories. The concentration of work in this branch suggests that multi-turn tool-use is an active research direction, though the specific leaf is moderately sized rather than overcrowded.

The taxonomy reveals several neighboring research directions that contextualize this work. Adjacent leaves address context management and long-horizon tool-use, efficiency and strategic tool invocation, and user-interactive adaptive agents—all within the same parent branch. Nearby branches explore code interpreter integration, synthetic environment generation, and specialized domain applications. The scope note for the original leaf emphasizes 'sequential tool invocations across multiple turns using RL with outcome or step-wise rewards,' distinguishing it from single-turn approaches and pure reasoning tasks. This positioning suggests the paper engages with a well-defined but not isolated research problem.

Among the three contributions analyzed, the literature search examined twenty-eight candidates total. The theoretical link between policy entropy and training stability was examined against ten candidates with no clear refutations found. The ResT algorithm itself was examined against eight candidates, with one refutable match identified, suggesting some overlap with prior gradient reshaping or token-weighting approaches. The optimal entropy-aware reweighting scheme was examined against ten candidates with no refutations. The limited search scope—top-K semantic search plus citation expansion—means these statistics reflect a targeted rather than exhaustive comparison, particularly relevant given the moderately populated research area.

Based on the available signals, the work appears to make incremental but meaningful contributions within an active research direction. The theoretical entropy-stability connection and the specific reweighting scheme show no clear prior overlap among the examined candidates, while the core ResT algorithm has at least one potentially overlapping prior work. The taxonomy structure indicates this is neither a sparse frontier nor an overcrowded space, suggesting room for refinement of existing approaches. However, the analysis is constrained by the limited search scope and does not capture the full landscape of related work.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
28
Contribution Candidate Papers Compared
1
Refutable Paper

Research Landscape Overview

Core task: Optimizing tool-use policies in large language models through reinforcement learning. The field has evolved into a rich landscape organized around several complementary directions. At the highest level, one finds branches dedicated to core RL algorithms and training frameworks that underpin reasoning improvements, alongside specialized techniques for LLM training and offline or preference-based methods. Another set of branches focuses on the nature of the tasks themselves: tool-integrated reasoning and code execution, multi-turn agentic workflows, search and knowledge acquisition, and domain-specific applications. Meanwhile, hybrid approaches that blend LLM guidance with RL, as well as planning and exploration strategies for decision-making agents, round out the taxonomy. Works such as DeepSeek-R1[1] and Kimi[4] illustrate how core RL training can yield strong reasoning capabilities, while efforts like Retool[2] and Teaching LLMs Reason[3] emphasize structured policy optimization. Benchmarking and evaluation frameworks provide the empirical grounding needed to compare these diverse methods. Within this landscape, multi-turn agentic tool-use and workflow optimization has emerged as a particularly active area, addressing the challenge of sequential decision-making over extended interactions. ResT[0] sits squarely in this branch, focusing on refining policies that orchestrate tool calls across multiple steps. It shares thematic ground with neighbors such as Tool-Star[11], RLFactory[15], and ToolRL[16], all of which explore how RL can guide agents through complex tool-use sequences. Compared to StepTool[43] and Synthetic Multi-Step[44], which emphasize step-level supervision or synthetic data generation, ResT[0] places greater emphasis on end-to-end policy learning that balances exploration with effective credit assignment over longer horizons. This positioning highlights an ongoing tension in the field: whether to rely on dense intermediate signals or to let RL discover multi-step strategies more autonomously, a question that continues to shape research across several branches.

Claimed Contributions

Theoretical link between policy entropy and training stability

The authors develop a formal variance analysis demonstrating that lower token-level policy entropy correlates with reduced variance in policy-gradient updates. This theoretical framework reveals that reward mass in tool-use tasks concentrates on structured, low-entropy tokens such as tool names, arguments, and format tags.

10 retrieved papers
ResT: Entropy-aware token-level gradient reshaping with curriculum learning

The authors introduce ResT, an algorithm that reshapes policy gradients using entropy-informed token reweighting combined with a lightweight curriculum. This mechanism initially emphasizes structural tokens and progressively shifts focus to reasoning tokens, enabling a smooth transition from structural correctness to semantic reasoning while stabilizing convergence in multi-turn tool-use tasks.

8 retrieved papers
Can Refute
Optimal entropy-aware reweighting scheme for variance reduction

The authors derive a closed-form optimal reweighting scheme that minimizes policy-gradient variance by down-weighting sequence positions with larger intrinsic variance contributions. This theoretical result provides a principled foundation for the entropy-based token reweighting used in ResT.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Theoretical link between policy entropy and training stability

The authors develop a formal variance analysis demonstrating that lower token-level policy entropy correlates with reduced variance in policy-gradient updates. This theoretical framework reveals that reward mass in tool-use tasks concentrates on structured, low-entropy tokens such as tool names, arguments, and format tags.

Contribution

ResT: Entropy-aware token-level gradient reshaping with curriculum learning

The authors introduce ResT, an algorithm that reshapes policy gradients using entropy-informed token reweighting combined with a lightweight curriculum. This mechanism initially emphasizes structural tokens and progressively shifts focus to reasoning tokens, enabling a smooth transition from structural correctness to semantic reasoning while stabilizing convergence in multi-turn tool-use tasks.

Contribution

Optimal entropy-aware reweighting scheme for variance reduction

The authors derive a closed-form optimal reweighting scheme that minimizes policy-gradient variance by down-weighting sequence positions with larger intrinsic variance contributions. This theoretical result provides a principled foundation for the entropy-based token reweighting used in ResT.

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