Integrating supervised fine-tuning (SFT) and reinforcement learning (RL) has become a common post-training paradigm for improving the reasoning capabilities of large language models. Existing methods typically rely on sequential SFT–RL pipelines or static loss weights, overlooking sample-level heterogeneity and evolving learning states. We propose Sample Learning State (SLS), a rollout-based characterization of each training sample using two complementary signals: sample mastery and exploration-trajectory dispersion. These signals estimate whether reward-driven updates are reliable for the core task and whether current rollouts provide informative contrast for RL optimization, respectively. Based on SLS, we design a state-aware coefficient that dynamically balances SFT and RL losses, increasing the RL weight only when the model demonstrates sufficient core-task competence and the sampled trajectories provide useful reward contrast. Experiments on Qwen2.5-Math models show that SLS achieves state-of-the-art results on four in-distribution and two out-of-distribution mathematical reasoning benchmarks. In multi-reward settings, SLS preserves core reasoning performance while mitigating reward hacking induced by auxiliary rewards.

Long chain-of-thought (CoT) reasoning in large language models (LLMs) improves performance on complex tasks but incurs substantial inference cost, although many queries do not require extended computation. Existing approaches typically reduce this cost via uniform length penalties or fixed computation budgets, but lack length constraints aligned with the model’s own query-dependent distribution of successful reasoning lengths. We propose a reinforcement learning framework that aligns the generated reasoning length with query-dependent successful computation scales estimated from verified trajectories. By providing adaptive guidance derived from the distribution of successful reasoning lengths, our method avoids the limitations of global length regularization. Experiments on mathematical reasoning benchmarks demonstrate improved accuracy–efficiency trade-offs, reducing unnecessary reasoning while maintaining or improving task performance.

Existing table understanding methods face challenges due to complex table structures and intricate logical reasoning. While supervised finetuning (SFT) dominates existing research, reinforcement learning (RL), such as Group Relative Policy Optimization (GRPO), has shown promise but struggled with low initial policy accuracy and coarse rewards in tabular contexts. In this paper, we introduce Table-R1, a three-stage RL framework that enhances multimodal table understanding through: (1) Warm-up that prompts initial perception and reasoning capabilities, (2) Perception Alignment GRPO (PA-GRPO), which employs continuous Tree-Edit-Distance Similarity (TEDS) rewards for recognizing table structures and contents, and (3) Hint-Completion GRPO (HC-GRPO), which utilizes fine-grained rewards of residual steps based on the hint-guided question. Extensive experiments demonstrate that Table-R1 can boost the model’s table reasoning performance obviously on both held-in and held-out datasets, outperforming SFT and GRPO largely. Notably, Qwen2-VL-7B with Table-R1 surpasses larger specific table understanding models (e.g., Table-LLaVA 13B), even achieving comparable performance to the closed-source model GPT-4o on held-in datasets, demonstrating the efficacy of each stage of Table-R1 in overcoming initialization bottlenecks and reward sparsity, thereby advancing robust multimodal table understanding.

Information extraction (IE) encounters challenges due to the variety of schemas and objectives that differ across tasks. Recent advancements hint at the potential for universal approaches to model such tasks, referred to as Universal Information Extraction (UIE). While handling diverse tasks in one model, their generalization is limited since they are actually learning task-specific knowledge.In this study, we introduce an innovative paradigm known as TRUE-UIE, wherein all IE tasks are aligned to learn the same goals: extracting mention spans and two universal relations named NEXT and IS. During the decoding process, the NEXT relation is utilized to group related elements, while the IS relation, in conjunction with structured language prompts, undertakes the role of type recognition. Additionally, we consider the sequential dependency of tokens during span extraction, an aspect often overlooked in prevalent models.Our empirical experiments indicate that TRUE-UIE achieves state-of-the-art performance on established benchmarks encompassing 16 datasets, spanning 7 diverse IE tasks. Further evaluations reveal that our approach effectively share knowledge between different IE tasks, showcasing significant transferability in zero-shot and few-shot scenarios.

“层次结构极限多标签文本分类是自然语言处理研究领域中一个重要而又具有挑战性的课题。该任务类别标签数量巨大且自成体系,标签与标签之间还具有不同层级间的依赖关系或同层次间的相关性,这些特性进一步增加了任务难度。该文提出将层次结构极限多标签文本分类任务视为序列转换问题,将输出标签视为序列,从而可以直接从数十万标签中生成与文本相关的类别标签。通过软约束机制和词表复合映射在解码过程中利用标签之间的层次结构与相关信息。实验结果表明,该文提出的方法与基线模型相比取得了有意义的性能提升。进一步分析表明,该方法不仅可以捕获利用不同层级标签之间的上下位关系,还对极限多标签体系自身携带的噪声具有一定容错能力。”