Zhuoshi Pan, Qizhi Pei, Yu Li, Zinan Tang Alibaba Group, QiYao Sun, H. Vicky Zhao Tsinghua University, Tsinghua University, Conghui He Shanghai AI Lab, Lijun Wu Shanghai Artificial Intelligence Laboratory
Recent Large Reasoning Models (LRMs) have achieved remarkable progress, yet their evaluation still relies on a narrow paradigm: evaluating one question at a time. This single-question setup suffers from two major limitations: (1) vulnerability to data contamination and diminishing difficulty, forcing costly creation of new questions with significant human effort, (2) failure to evaluate models under multi-context pressure, a key requirement for real-world deployment. To bridge this gap, we present REST (Reasoning Evaluation through Simultaneous Testing), a stress-testing framework that exposes LRMs to multiple problems simultaneously. Beyond basic reasoning, REST evaluates two under-tested capabilities: contextual priority allocation and robustness against contextual interference. Our evaluation of more than 30 advanced reasoning models on 9 reasoning benchmarks reveals several striking findings: Even state-of-the-art (SOTA) models such as DeepSeek-R1 exhibit substantial performance degradation under stress testing, challenging the prevailing assumption that “LLMs are multi-problem solvers”. Crucially, REST demonstrates stronger discriminative power than existing benchmarks, revealing performance gaps among models that exhibit similar, near-ceiling performance under traditional evaluation. Some key insights emerge from our analysis: (1) the “overthinking trap” is a critical factor contributing to the performance degradation; (2) models trained with the “Long2Short” technique preserve more of their single-problem accuracy under REST, outperforming their standard-trained counterparts. These results establish REST as a cost-efficient, future-proof evaluation paradigm while reducing reliance on continuous human annotation. Code is available at https://github.com/opendatalab/REST.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Zheng Liu, Mengjie Liu, Siwei Wen Beijing University of Aeronautics and Astronautics, Mengzhang Cai, Bin Cui Peking University, Conghui He Shanghai AI Lab, Wentao Zhang
Using entropy as a measure of heterogeneity to guide optimization has emerged as a crucial research direction in Reinforcement Learning for LLMs. However, existing methods typically treat it as a discrete filter or post-hoc regulator rather than a core optimization driver. To fully leverage the potential of entropy and achieve fine-grained regulation, we introduce Heterogeneous Adaptive Policy Optimization (HAPO), a token-aware algorithm that continuously adapts optimization dynamics based on token-level entropy throughout the entire training process. Our algorithm includes four key components: (1) Adaptive Temperature Sampling that adjusts sampling temperature in real time, promoting exploration at high-entropy tokens. (2) Token-Level Group Average Advantage Estimation that estimates advantages at token level, accounting for sequence-length effects while preserving non-biased treatment.(3) Differential Advantage Redistribution that leverages entropy and importance ratios to adjust advantages for tokens with clear signals. (4) Asymmetric Adaptive Clipping that dynamically adjusts clipping boundaries based on token-level entropy. Through systematic investigation of entropy, we embed token-level treatment into every stage. Extensive experiments on mathematical reasoning, code, and logic tasks across multiple models demonstrate HAPO’s consistent superiority over DAPO.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Chart reasoning is a critical capability for Vision Language Models (VLMs). However, the development of open-source models is severely hindered by the lack of high-quality training data. Existing datasets suffer from a dual challenge: synthetic charts are often simplistic and repetitive, while the associated QA pairs are prone to hallucinations and lack the reasoning depth required for complex tasks. To bridge this gap, we propose ChartVerse, a scalable framework designed to synthesize complex charts and reliable reasoning data from scratch. (1) To address the bottleneck of simple patterns, we first introduce Rollout Posterior Entropy (RPE), a novel metric that quantifies chart complexity. Guided by RPE, we develop complexity-aware chart coder to autonomously synthesize diverse, high-complexity charts via executable programs. (2) To guarantee reasoning rigor, we develop truth-anchored inverse QA synthesis. Diverging from standard generation, we adopt an answer-first paradigm: we extract deterministic answers directly from the source code, generate questions conditional on these anchors, and enforce strict consistency verification. To further elevate difficulty and reasoning depth, we filter samples based on model fail-rate and distill high-quality Chain-of-Thought (CoT) reasoning. We curate ChartVerse-SFT-600K and ChartVerse-RL-40K using Qwen3-VL-30B-A3B-Thinking as the teacher. Experimental results demonstrate that ChartVerse-8B achieves state-of-the-art performance, notably surpassing its teacher and rivaling the stronger Qwen3-32B-Thinking.
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cs.CL · cs.CV
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Post-training data plays a pivotal role in shaping the capabilities of Large Language Models (LLMs), yet datasets are often treated as isolated artifacts, overlooking the systemic connections that underlie their evolution. To disentangle these complex relationships, we introduce the concept of data lineage to the LLM ecosystem and propose an automated multi-agent framework to reconstruct the evolutionary graph of dataset development. Through large-scale lineage analysis, we characterize domain-specific structural patterns, such as vertical refinement in Math-oriented datasets and horizontal aggregation in General-domain corpora. Moreover, we uncover pervasive systemic issues, including structural redundancy induced by implicit dataset intersections and the propagation of benchmark contamination along lineage paths. To demonstrate the practical value of lineage analysis for data construction, we leverage the reconstructed lineage graph to create a lineage-aware diversity-oriented dataset. By anchoring instruction sampling at upstream leaf sources, this approach mitigates downstream homogenization and hidden redundancy, yielding a more diverse post-training corpus. We further highlight lineage-centric analysis as an efficient and robust topological alternative to sample-level dataset comparison for large-scale data ecosystems. By grounding data construction in explicit lineage structures, our work advances post-training data curation toward a more systematic and controllable paradigm.
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cs.CL · cs.MA
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Lijun Wu Shanghai AI Laboratory, Wentao Zhang Peking University, Conghui He Shanghai AI Lab
As the development of Large Language Models (LLMs) matures, the focus of the research community is undergoing a critical shift from a purely model-centric to a data-centric paradigm. It is now evident that the quality, diversity, and composition of training data—not merely its scale—are the primary drivers of a model’s advanced capabilities, from complex reasoning to reliable instruction following. However, acquiring and curating such high-quality data remains a significant bottleneck. This tutorial provides a comprehensive and practical guide to the state-of-the-art in data research directions for LLMs. We structure the tutorial around the two core pillars of modern data strategy: intelligent data selection and advanced data synthesis. In the first part, we delve into methods for curating the most valuable information from vast, noisy datasets, covering techniques like LLM-as-a-judge for automated quality filtering and active learning for maximizing annotation efficiency. The second part explores the synthetic data revolution, detailing paradigms that range from generating complex reasoning traces (e.g., Chain-of-Thought) to deploying sophisticated multi-agent workflows that can autonomously create high-quality, diverse instruction data from raw seeds. Finally, we will conclude with a practical overview of open-source tools and platforms that facilitate these data-centric workflows, empowering researchers and practitioners to build better models through better data. Attendees will leave with a principled framework and actionable insights for designing and implementing the advanced data strategies required to build the next generation of powerful, specialized, and aligned LLMs.
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cs.CL · cs.MA
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 5: Tutorial Abstracts) · 2026 · 正式发表