arXiv Machine Learning By Aijia Cheng, Kailong Wang, Ling Shi, Yongxin Zhao

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

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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