Papers by Mengyi Deng
When Inverse Data Outperforms: Exploring the Pitfalls of Mixed Data in Multi-Stage Fine-Tuning (2025.findings-emnlp)
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| Challenge: | Existing methods for o1-level performance focus on unidirectional supervised fine-tuning (SFT), overlooking the intricate interplay between diverse reasoning patterns. |
| Approach: | They construct a reverse reasoning dataset and examine how it is supervised . they find that naively mixing forward and reverse data during SFT weakens the directional distinction . |
| Outcome: | The proposed model improves accuracy by 1.6%–6.8% over a standard model. |
DGPO: Beyond Pairwise Preferences with Directional Consistent Groupwise Optimization (2026.findings-acl)
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| Challenge: | Existing methods for directional consistency alignment of large language models are limited . a recent study suggests reverse supervision as a complement to forward reasoning . |
| Approach: | They propose a framework that aggregates supervision signals at the group level and explicitly models direction-aware alignment through multi-candidate comparisons. |
| Outcome: | The proposed framework achieves 3.2% accuracy improvement across five benchmarks and multiple datasets. |