• Computer Science > Computation and Language [Submitted on 4 Feb 2026] Title:ImpRIF: Stronger Implicit Reasoning Leads to Better Complex Instruction Following 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

Article Summaries:

  • Computer Science > Computation and Language [Submitted on 4 Feb 2026] Title:ImpRIF: Stronger Implicit Reasoning Leads to Better Complex Instruction Following 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 i

Sources: