Scalable and Distributed Alignment Mechanisms for Autonomous and Controllable English Text Generation

INTRODUCTION: Large-scale English text generation models have shown remarkable capabilities across diverse applications, yet they still face significant challenges in controllability and alignment, especially when handling complex, multi-constraint instructions that require precise intent following and output consistency. OBJECTIVES: To address the lack of a systematic end-to-end alignment framework for large models, this work aims to develop an autonomous and controllable mechanism that ensures high-fidelity generation under intricate user directives. METHODS: We propose a unified alignment architecture composed of three synergistic modules: (1) an instruction parser that converts raw instructions and constraints into structured task representations; (2) a constraint-aware reinforcement learning controller that optimizes token selection via learnable rewards based on alignment and constraint metrics; and (3) a fine-grained aligner that enforces local semantic consistency through differentiable cross-attention between input and output. RESULTS: Evaluated on a custom Instruction-Gen dataset and public benchmarks, our method achieves 84.7% intent alignment accuracy and 88.3% constraint satisfaction, improving by 6.9 and 7.1 percentage points over the PPO-pt baseline, respectively (p < 0.01), while maintaining comparable generation quality (BLEU, ROUGE-L) and textual diversity. CONCLUSION: This work provides a systematic solution for controllable text generation under complex instructions, offering both methodological advances in alignment and practical utility in applications such as intelligent writing and dialogue systems.