R2IF: Aligning Reasoning with Decisions via Composite Rewards for Interpretable LLM Function Calling

• URL: http://arxiv.org/abs/2604.20316v1
• Date: Wed, 22 Apr 2026 08:13:24 GMT
• Title: R2IF: Aligning Reasoning with Decisions via Composite Rewards for Interpretable LLM Function Calling
• Abstract summary: We propose R2IF, a reasoning-aware RL framework for interpretable function calling.<n> Experiments on BFCL/ACEBench show R2IF outperforms baselines by up to 34.62%.
• Score: 7.385881045816043
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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