CRGC: Resolving Instruction Following Challenges in Large Language Reasoning Models via Bridging Constraints

This article introduces the CRGC (Constraint Relationship Graph Completion) framework, which represents instructions as constraint knowledge graphs, explicitly models relationships between constraints, and discovers "bridging constraints" to help models understand and balance multiple requirements. The framework reduces constraint violation rates by 39% across three datasets. Source: arXiv authors, original title: Bridging Auxiliary Constraints to Resolve Instruction Following in Large Reasoning Models, link: http://arxiv.org/abs/2606.03624v1, published on 2026-06-02T13:23:28Z.

Large Reasoning Models (LRMs) face significant challenges in following complex instructions: they cannot satisfy all independent constraints simultaneously, or struggle to balance conflicting constraints. Researchers formalize this as the Constraint Adherence Problem (CAP), with the core difficulty being that the implicit constraint relationships in natural language instructions are not explicitly annotated, requiring models to identify and coordinate them on their own.

The CRGC framework does not rely on additional training data or fine-tuning. Its core is to represent instructions as constraint knowledge graphs—nodes represent independent constraints, and edges represent relationships between constraints. By explicitly modeling these relationships, it identifies difficulties in constraint adherence and discovers "bridging constraints."

Bridging constraints assist models through two mechanisms: 1. Enhancing salience: Supplementary explanations make main constraints more prominent; 2. Promoting compatibility: Reconciling conflicting constraints to help find paths that satisfy all of them. This is analogous to the auxiliary thinking strategies humans use to solve complex problems.

Traditional methods (supervised fine-tuning, reinforcement learning, prompt engineering) generally improve instruction following capabilities, while CRGC is specifically optimized for constraint satisfaction: it does not require modifying model parameters or additional training data. Its advantages include low deployment cost, strong interpretability, and preservation of original reasoning capabilities.

Validation results across three datasets: constraint violation rates reduced by 39%; reasoning capabilities remain unchanged; cross-dataset generalization effects are consistent. This proves that CRGC has significant practical value.

For prompt engineers: Provides a systematic analysis method to improve complex instructions; For AI product developers: Reveals new dimensions in instruction design, allowing the introduction of automated constraint analysis functions; For research: Opens up new paths for applying knowledge graphs to instruction following, with future directions including exploring dynamic bridging constraint generation.