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[2602.21228] ImpRIF: Stronger Implicit Reasoning Leads to Better Complex Instruction Following
Summary
The paper presents ImpRIF, a method to enhance large language models' implicit reasoning capabilities, improving their performance in following complex instructions.
Why It Matters
As the complexity of tasks increases in AI applications, the ability to understand and follow intricate instructions becomes critical. ImpRIF addresses this need by formalizing implicit reasoning, which can lead to significant advancements in AI's operational efficiency and reliability.
Key Takeaways
- ImpRIF enhances LLMs' understanding of implicit reasoning in instructions.
- The method utilizes verifiable reasoning graphs for programmatic verification.
- Substantial performance improvements were observed on five complex instruction benchmarks.
- Fine-tuning with graph reasoning and reinforcement learning is central to the approach.
- The project will be open-sourced, promoting further research and development.
Computer Science > Computation and Language arXiv:2602.21228 (cs) [Submitted on 4 Feb 2026] Title:ImpRIF: Stronger Implicit Reasoning Leads to Better Complex Instruction Following Authors:Yuancheng Yang, Lin Yang, Xu Wang, Chao Tong, Haihua Yang View a PDF of the paper titled ImpRIF: Stronger Implicit Reasoning Leads to Better Complex Instruction Following, by Yuancheng Yang and Lin Yang and Xu Wang and Chao Tong and Haihua Yang View PDF HTML (experimental) Abstract:As applications of large language models (LLMs) become increasingly complex, the demand for robust complex instruction following capabilities is growing accordingly. We argue that a thorough understanding of the instruction itself, especially the latent reasoning structure embedded between the lines, is crucial for improving instruction following. Therefore we target complex instructions that involve implicit reasoning, intricate logical relations, and multi-constraint dependencies. We propose ImpRIF, a method to enhance LLMs' understanding of implicit reasoning instructions, thereby improving its ability to follow complex instructions. We formalize such instructions as verifiable reasoning graphs, enabling programmatic verification and graph-driven chain-of-thought reasoning. Based on this formulation, we synthesize large-scale single- and multi-turn data, propose fine-tuning with graph reasoning, and apply reinforcement learning to explicitly train models to reason along the graph. On five complex instruction...
