R2IF: Aligning Reasoning with Decisions via Composite Rewards for Interpretable LLM Function Calling (2026.acl-long)
Copied to clipboard
| Challenge: | Existing RL-based approaches to function calling are misaligned between reasoning processes and tool-call decisions. |
| Approach: | They propose a reasoning-aware RL framework for interpretable function calling . they integrate a composite reward integrating format/correctness constraints, CER, and SMV . |
| Outcome: | Experiments on BFCL/ACEBench show R2IF outperforms baselines by 34.62% with positive Average CoT Effectiveness. |
Similar Papers
Improving Large Language Models Function Calling and Interpretability via Guided-Structured Templates (2025.emnlp-main)
Copied to clipboard
Hy Dang, Tianyi Liu, Zhuofeng Wu, Jingfeng Yang, Haoming Jiang, Tao Yang, Pei Chen, Zhengyang Wang, Helen Wang, Huasheng Li, Bing Yin, Meng Jiang
| Challenge: | Large language models (LLMs) have strong reasoning and tool-use capabilities, yet fail in real-world tool-interactions due to incorrect parameterization, poor tool selection, or misinterpretation of user intent. |
| Approach: | They propose a curriculum-inspired framework that leverages structured reasoning templates to guide LLMs through more deliberate step-by-step instructions for generating function calls. |
| Outcome: | The proposed framework reduces tool-use errors and improves interpretability and transparency of tool-using agents. |
Bridging Reasoning and Action: Hybrid LLM–RL Framework for Efficient Cross-Domain Task-Oriented Dialogue (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing methods to solve cross-domain task-oriented dialogues are brittle when cross- domain constraints are not directly grounded in surface text or require commonsense inference. |
| Approach: | They propose a framework that makes LLM-derived constraint reasoning usable for RL. |
| Outcome: | Experiments show that the proposed framework outperforms single-model baselines on long-horizon tasks. |
Enhancing Function-Calling Capabilities in LLMs: Strategies for Prompt Formats, Data Integration, and Multilingual Translation (2025.naacl-industry)
Copied to clipboard
| Challenge: | Large language models (LLMs) have significantly advanced autonomous agents, particularly in zero-shot tool usage, also known as function calling. |
| Approach: | They propose to integrate function descriptions into prompt formats and introduce a new Decision Token for conditional prompts. |
| Outcome: | The proposed decision token improves function-calling accuracy and relevance detection and a translation pipeline overcomes multilingual limitations. |
Crossing the Reward Bridge: Expanding Reinforcement Learning with Verifiable Rewards Across Diverse Domains (2026.acl-long)
Copied to clipboard
| Challenge: | Reinforcement learning with verifiable rewards (RLVR) has been effective on structured tasks, but its reliance on simple, rule-based verifiers creates a bottleneck. |
| Approach: | They propose a framework that uses a generative verifier to provide soft, probabilistic rewards. |
| Outcome: | The proposed framework outperforms existing models up to 10x their size and can be scalable and effective. |
GRPO-CARE: Consistency-Aware Reinforcement Learning for Multimodal Reasoning (2026.findings-acl)
Copied to clipboard
| Challenge: | Recent reinforcement learning approaches have advanced reasoning in Large Language Models (LLMs), yet their adaptation to multimodal LLMs remains underexplored. |
| Approach: | They propose a reinforcement learning framework that eliminates KL penalties and rewards consistency . they propose GRPO-CARE, which outperforms standard GR PO, with a base reward for accuracy and an adaptive bonus for consistency. |
| Outcome: | The proposed framework outperforms standard GRPO on the most difficult evaluation level and reasoning consistency test benchmarks. |
R-Search: Empowering LLM Reasoning with Search via Multi-Reward Reinforcement Learning (2026.findings-acl)
Copied to clipboard
Qingfei Zhao, Ruobing Wang, Dingling Xu, Daren Zha, Ma Bowen, Zhichun Wang, Shijie Jia, Limin Liu, Xin Wang
| Challenge: | Large language models (LLMs) have demonstrated impressive capabilities in multi-step and long-chain reasoning, but extending their reasoning capabilities to encompass deep interactions with search remains a non-trivial challenge. |
| Approach: | They propose a framework for Reasoning–Search integration that integrates multi-reward signals to optimize the reasoning–search interaction trajectories. |
| Outcome: | Experiments on seven datasets show that R-Search significantly outperforms mainstream RAG baselines. |
Lost in Execution: On the Multilingual Robustness of Tool Calling in Large Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | Large Language Models (LLMs) are increasingly deployed as agents that invoke external tools through structured function calls. |
| Approach: | They introduce a diagnostic benchmark and conduct a systematic evaluation of multilingual tool calling across Chinese, Hindi, and the low-resource language Igbo. |
| Outcome: | The proposed benchmarks show that multilingual tool calling fails despite correct intent understanding and tool selection. |
Verifying the Subjective: Structured Multilingual Rewards for Low-Resource Alignment (2026.findings-acl)
Copied to clipboard
| Challenge: | Structured Multilingual Reward Modeling Framework extends Reinforcement Learning with Verifiable Rewards (RLVR) to subjective and open-ended tasks. |
| Approach: | They propose a framework that extends Reinforcement Learning with Verifiable Rewards to subjective and open-ended tasks. |
| Outcome: | The proposed framework improves reasoning capability and response quality on 7 tasks across 50 low-resource languages. |
Reason-KE++: Aligning the Process, Not Just the Outcome, for Faithful LLM Knowledge Editing (2026.findings-acl)
Copied to clipboard
| Challenge: | Current methods for modifying parameters to integrate new knowledge are not accurate enough. |
| Approach: | They propose an SFT+RL framework that instills process-level faithfulness by a stage-aware Reward mechanism and a Stage-assisted Reward Mechanism. |
| Outcome: | The proposed framework instills process-level faithfulness while boosting final accuracy. |
DICE: Structured Reasoning in LLMs through SLM-Guided Chain-of-Thought Correction (2025.emnlp-main)
Copied to clipboard
| Challenge: | Large language models (LLMs) often prioritize reasoning over adherence to detailed instructions due to high computational costs and limited parameter access. |
| Approach: | They propose a lightweight framework that guides small language models to refine LLMs’ outputs through chain-of-thought correction. |
| Outcome: | The proposed framework improves the average format accuracy and content correctness of LLM outputs by 35.4% and 29.4%, respectively, achieving state-of-the-art (SOTA) performance over other competitive baselines. |