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R2IF addresses the critical misalignment between reasoning traces and tool-execution decisions in LLMs by introducing a composite reward framework optimized via GRPO. This approach significantly boosts function-calling accuracy and interpretability on standard benchmarks, offering a more reliable foundation for deploying tool-augmented models. The method's focus on aligning Chain-of-Thought effectiveness with actual outcomes provides a concrete path forward for improving the trustworthiness of agentic systems.
R2IF: Aligning Reasoning with Decisions via Composite Rewards for Interpretable LLM Function Calling
arXiv:2604.20316v2 Announce Type: replace Abstract: Function calling empowers large language models (LLMs) to interface with external tools, yet existing RL-based approaches suffer from misalignment between reasoning processes and tool-call decisions. We propose R2IF, a reasoning-aware RL framework for interpretable function calling, adopting a composite reward integrating format/correctness constraints, Chain-of-Thought Effectiveness Reward (CER), and Specification-Modification-Value (SMV) reward, optimized via GRPO. Experiments on BFCL/ACEBench show R2IF outperforms baselines by up to 34.62% (Llama3.2-3B on BFCL) with positive Average CoT Effectiveness (0.05 for Llama3.2-3B), enhancing both function-calling accuracy and interpretability for reliable tool-augmented LLM deployment.
Abstract:Function calling empowers large language models (LLMs) to interface with external tools, yet existing RL-based approaches suffer from misalignment between reasoning processes and tool-call decisions. We propose R2IF, a reasoning-aware RL framework for interpretable function calling, adopting a composite reward integrating format/correctness constraints, Chain-of-Thought Effectiveness Reward (CER), and Specification-Modification-Value (SMV) reward, optimized via GRPO. Experiments on BFCL/ACEBench show R2IF outperforms baselines by up to 34.62% (Llama3.2-3B on BFCL) with positive Average CoT Effectiveness (0.05 for Llama3.2-3B), enhancing both function-calling accuracy and interpretability for reliable tool-augmented LLM deployment.
Machine Learning (cs.LG)
arXiv:2604.20316 [cs.LG]
(or arXiv:2604.20316v2 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2604.20316 Focus to learn more arXiv-issued DOI via DataCite
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