Papers by Beichen Zhang
Debiased Contrastive Learning of Unsupervised Sentence Representations (2022.acl-long)
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| Challenge: | Recent studies have shown that contrastive learning improves pre-trained language models to derive high-quality sentence representations. |
| Approach: | They propose a framework to punish false negatives and generate noise-based negatives to guarantee the uniformity of the representation space. |
| Outcome: | The proposed framework improves pre-trained language models while pushing apart irrelevant negatives to guarantee the uniformity of the representation space. |
Hidden States as Early Signals: Step-level Trace Evaluation and Pruning for Efficient Test-Time Scaling (2026.findings-acl)
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| Challenge: | Existing approaches to speed up parallel scaling have relied on similarity-based or confidence-based pruning, but these signals do not reliably indicate trace quality. |
| Approach: | They propose a pruning framework that evaluates reasoning steps using hidden states and dynamically prunes unpromising traces during generation. |
| Outcome: | The proposed framework reduces end-to-end inference latency by 45%–70% on average compared to self-consistency while improving reasoning accuracy. |
Think Beyond Words: Exploring Context-Relevant Visual Commonsense for Diverse Dialogue Generation (2022.findings-emnlp)
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| Challenge: | Existing approaches to generate intelligent open-domain dialogue agents only consider auxiliary commonsense stored in pure text, ignoring grounding information from the external visual world. |
| Approach: | They propose a VIsual Commonsense enhanced dialogue generaTOR that exploits auxiliary commonsense from images related to context to generate coherent and informative responses. |
| Outcome: | The proposed method outperforms the latest competitive methods in terms of coherence and diversity on two public datasets. |
ProcessBench: Identifying Process Errors in Mathematical Reasoning (2025.acl-long)
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Chujie Zheng, Zhenru Zhang, Beichen Zhang, Runji Lin, Keming Lu, Bowen Yu, Dayiheng Liu, Jingren Zhou, Junyang Lin
| Challenge: | Existing models fail to generalize to more challenging math problems, authors say . existing benchmarks related to assessing language models' reasoning process are limited . |
| Approach: | They propose a tool to measure language models' ability to identify erroneous steps in reasoning . they use two types of models: process reward models and critic models . |
| Outcome: | The proposed model outperforms existing models in evaluating language models' reasoning process . the best open-source model has demonstrated the critique capability competitive with the proprietary model . |
S2O: Early Stopping for Sparse Attention via Online Permutation (2026.acl-long)
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| Challenge: | Existing block-granularity sparsification can reduce latency, but coarse blocks impose an intrinsic sparsity ceiling. |
| Approach: | They propose a method that performs early stopping for sparse attention via online permutation. |
| Outcome: | The proposed approach reduces the complexity of the model and its performance. |
START: Self-taught Reasoner with Tools (2025.emnlp-main)
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Chengpeng Li, Mingfeng Xue, Zhenru Zhang, Jiaxi Yang, Beichen Zhang, Bowen Yu, Binyuan Hui, Junyang Lin, Xiang Wang, Dayiheng Liu
| Challenge: | Large Reasoning Models (LRMs) have demonstrated remarkable capabilities in complex reasoning through long chain-of-thought, yet they struggle with precise computations and algorithmic operations. |
| Approach: | They propose a training-free approach that activates LRMs’ latent tool-use capabilities through artificial hints and a framework that enables models to learn effective tool utilization through diverse hint patterns and rejection-based data synthesis. |
| Outcome: | Experiments show that START significantly improves state-of-the-art LRMs across challenging benchmarks, including competition-level mathematics (AMC23: 95.0%, AIME24: 75.6%) and graduate-level science questions (GPQA: 64.6%). |
The Lessons of Developing Process Reward Models in Mathematical Reasoning (2025.findings-acl)
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Zhenru Zhang, Chujie Zheng, Yangzhen Wu, Beichen Zhang, Runji Lin, Bowen Yu, Dayiheng Liu, Jingren Zhou, Junyang Lin
| Challenge: | a recent study shows that process reward models can make mistakes, leading to wrong conclusions. |
| Approach: | They propose a consensus filtering mechanism that integrates MC estimation with LLM-as-a-judge to improve model performance and data efficiency. |
| Outcome: | The proposed model outperforms existing open-source alternatives and provides practical guidelines for future research. |
ChatCoT: Tool-Augmented Chain-of-Thought Reasoning on Chat-based Large Language Models (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) have excellent performance in evaluation benchmarks, but struggle in complex reasoning tasks. |
| Approach: | They propose a tool-augmented chain-of-thought reasoning framework for chat-based LLMs . they model chain- of-thoughting reasoning as multi-turn conversations to utilize tools . |
| Outcome: | The proposed framework can outperform state-of-the-art models on complex reasoning tasks. |