Papers by Yongjiang Liu
TTVS: Boosting Self-Exploring Reinforcement Learning via Test-time Variational Synthesis (2026.findings-acl)
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| Challenge: | Existing test-time methods are limited in specialized or novel domains where supervision is prohibitively expensive or unavailable. |
| Approach: | They propose a framework that augments training stream from unlabeled test queries. |
| Outcome: | Extensive experiments show TTVS outperforms state-of-the-art RL-based techniques on unlabeled test-time data. |
Think How to Think: Mitigating Overthinking with Autonomous Difficulty Cognition in Large Reasoning Models (2026.acl-long)
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| Challenge: | Recent Large Reasoning Models (LRMs) excel at complex reasoning tasks but often suffer from overthinking. |
| Approach: | They propose a two-stage fine-tuning strategy that progressively inspires LRMs’ difficulty cognition and redundancy cognition of LRM. |
| Outcome: | The proposed model significantly reduces inference costs by over 70% on easy tasks and 40% on complex ones without compromising performance. |