Papers by Yuewei Zhang
PVPO: Pre-Estimated Value-Based Policy Optimization for Agentic Reasoning (2026.findings-acl)
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| Challenge: | extending grouping-based methods to agentic reasoning presents unique challenges . frequent environment interactions and tool invocations render intra-group advantage estimation unstable . |
| Approach: | They propose a grouping-based method that uses a single round of rollouts to stabilize advantage estimation. |
| Outcome: | a new RL framework outperforms grouping-based methods in retrieval tasks and advanced mathematical reasoning benchmarks. |
Mixture-of-LoRAs: An Efficient Multitask Tuning Method for Large Language Models (2024.lrec-main)
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| Challenge: | Instruction Tuning has the potential to stimulate or enhance specific capabilities of large language models. |
| Approach: | They propose a mixture-of-LoRAs architecture which is a parameter-efficient tuning method designed for multi-task learning with LLMs. |
| Outcome: | The proposed method can be iteratively adapted to a new domain, enabling quick domain-specific adaptation. |
Beyond Stochastic Exploration: What Makes Training Data Valuable for Agentic Search (2026.findings-acl)
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| Challenge: | Existing RL-based search agents rely on stochastic exploration, leading to inefficient reasoning trajectories and unstable training. |
| Approach: | They propose a framework to enhance the performance and training stability of search agents by transforming raw reasoning trajectories into hierarchical experience knowledge. |
| Outcome: | The proposed framework exhibits strong cross-task and cross-algorithm generalizations on multiple complex agentic search and mathematical reasoning benchmarks. |
RASD: Retrieval-Augmented Speculative Decoding (2025.findings-acl)
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| Challenge: | Existing methods for generating draft tokens rely on lightweight draft models or additional model structures to generate tokens and retrieve context from databases. |
| Approach: | They propose to use a pruning method to enhance model-based speculative decoding by combining the best-fit model with the best retrieval tree. |
| Outcome: | The proposed method achieves state-of-the-art inference acceleration across tasks such as DocQA, Summary, Code, and In-Domain QA. |
AirRAG: Autonomous Strategic Planning and Reasoning Steer Retrieval Augmented Generation (2025.findings-emnlp)
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| Challenge: | Experimental results show the effectiveness of AirRAG on complex question-answering datasets. |
| Approach: | They propose a new thinking pattern that integrates autonomous strategic planning with efficient reasoning actions. |
| Outcome: | The proposed approach significantly activates intrinsic reasoning capabilities and expands the solution space of specific tasks via Monte Carlo Tree Search. |
FAQ: Mitigating Quantization Error via Regenerating Calibration Data with Family-Aware Quantization (2026.findings-acl)
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| Challenge: | representativeness and universality of calibration data remain a bottleneck in quantization accuracy. |
| Approach: | They propose a framework that leverages prior knowledge from LLMs to generate calibration samples . their framework reduces accuracy loss by up to 28.5% compared to baseline . |
| Outcome: | Experiments show that family-aware quantization reduces accuracy loss by up to 28.5% compared to baseline data. |