Abstract
The capabilities of Large Language Models (LLMs) are limited to some extent by pre-training, so some researchers optimize LLMs through post-training. Existing post-training strategies, such as memory-based retrieval or preference optimization, improve user alignment yet fail to enhance the model’s domain cognition. To bridge this gap, we propose a novel Dual-Phase Self-Evolution (DPSE) framework that jointly optimizes user preference adaptation and domain-specific competence. DPSE introduces a Censor module to extract multi-dimensional interaction signals and estimate satisfaction scores, which guide structured data expansion via topic-aware and preference-driven strategies. These expanded datasets support a two-stage fine-tuning pipeline: supervised domain grounding followed by frequency-aware preference optimization. Experiments across general NLP benchmarks and long-term dialogue tasks demonstrate that DPSE consistently outperforms Supervised Fine-Tuning, Preference Optimization, and Memory-Augmented baselines. Ablation studies validate the contribution of each module. In this way, our framework provides an autonomous path toward continual self-evolution of LLMs.- Anthology ID:
- 2026.findings-acl.37
- Volume:
[Findings of the Association for Computational Linguistics: ACL 2026](/volumes/2026.findings-acl/)- Month:
- July
- Year:
- 2026
- Address:
- San Diego, California, United States
- Editors:
[Maria Liakata](/people/maria-liakata/),[Viviane P. Moreira](/people/viviane-p-moreira/unverified/),[Jiajun Zhang](/people/jiajun-zhang/unverified/),[David Jurgens](/people/david-jurgens/)- Venue:
[Findings](/venues/findings/)- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 772–782
- Language:
- URL:
[https://aclanthology.org/2026.findings-acl.37/](https://aclanthology.org/2026.findings-acl.37/)- DOI:
- Cite (ACL):
- Haoran Sun, Zekun Zhang, and Shaoning Zeng. 2026. A Dual-Phase Self-Evolution Framework for Large Language Models. InFindings of the Association for Computational Linguistics: ACL 2026, pages 772–782, San Diego, California, United States. Association for Computational Linguistics. - Cite (Informal):
[A Dual-Phase Self-Evolution Framework for Large Language Models](https://aclanthology.org/2026.findings-acl.37/)(Sun et al., Findings 2026)- PDF:
[https://aclanthology.org/2026.findings-acl.37.pdf](https://aclanthology.org/2026.findings-acl.37.pdf)