Papers by Yige Yuan
Incentivizing Strong Reasoning from Weak Supervision (2026.eacl-long)
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| Challenge: | Large language models (LLMs) have demonstrated impressive performance on reasoning-intensive tasks, but enhancing their reasoning abilities typically relies on expensive high-quality demonstrations and reinforcement learning. |
| Approach: | They propose to incentivize reasoning abilities of large language models without expensive demonstrations and reinforcement learning. |
| Outcome: | The proposed model can recover 94% of the gains of expensive RL at a fraction of the cost. |
Do We Always Need Query-Level Workflows? Rethinking Agentic Workflow Generation for Multi-Agent Systems (2026.findings-acl)
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| Challenge: | Existing approaches generate workflows either at task level or query level, but their relative costs and benefits remain unclear. |
| Approach: | They propose a query-level workflow generation framework that generates tasks at task level and query level. |
| Outcome: | The proposed framework reduces token usage by up to 83% compared to existing approaches . it maintains competitive performance with an average degradation of just 0.61% compared with existing approaches across multiple datasets . |
How to Leverage Demonstration Data in Alignment for Large Language Model? A Self-Imitation Learning Perspective (2024.emnlp-main)
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| Challenge: | Existing methods for fine-tuning large language models are not suitable for task-dependent tasks. |
| Approach: | They propose a generalized self-imitation learning framework which aligns large language models with offline demonstration data. |
| Outcome: | The proposed framework outperforms baselines in many challenging benchmarks . it is available on github.com/tengxiao1/GSIL . |
From Outcomes to Processes: Guiding PRM Learning from ORM for Inference-Time Alignment (2025.acl-long)
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| Challenge: | Existing approaches to align large language models with human preferences suffer from inconsistent scoring and suboptimal alignment. |
| Approach: | They propose a dual-consistency framework that aligns partial sequences with human preferences. |
| Outcome: | The proposed framework significantly reduces granularity discrepancies and improves GPT-4 evaluation scores. |