Reinforcement learning with verifiable rewards (RLVR) is a standard post-training paradigm for large language models (LLMs), typically relying on group-wise reward and advantage normalization for stability. In set-valued multi-answer tasks, where multiple outputs may be simultaneously correct, this normalization can over-amplify a small number of early high-reward samples, suppressing learning signals from other valid generations and leading to overly concentrated updates. We propose Entropy-Aware Reshaping of Reinforcement Signals (EARS), a framework that reshapes how learning signals are normalized and aggregated. EARS uses token-level predictive entropy as an uncertainty cue to compute entropy-weighted reward statistics for advantage normalization, encouraging broader exploration and more balanced learning-signal allocation early in training. An adaptive decay schedule then anneals uncertainty-aware reweighting back to standard group normalization to ensure stable convergence. EARS further incorporates a correctness-gated multi-head process reward that provides auxiliary supervision on reasoning traces while remaining aligned with verifiable correctness. Experiments on MCTACO and MMLU-Multi using Qwen2.5-7B and Llama-3.1-8B-Instruct demonstrate consistent improvements in exact-set accuracy, training stability, and cross-dataset transfer performance on set-valued multi-answer reasoning.

Lexical Complexity Prediction (LCP) in real-world scenarios, particularly in language education, is often limited by the trade-off between overly simplified general models and unscalable personalized ones. To address this, we introduce LCP for Leveled Reading (LCP-LR), a task aiming to operationalize group-level personalization by identifying complex words relative to specific proficiency levels, thereby balancing pedagogical guidance and scalability. We construct the first large-scale benchmark dataset for this task, comprising 100,917 instance annotations across 1,401 leveled texts. Furthermore, we propose Dual-Prior Adversarial Learning (DPAL), a framework that incorporates word- and text-level difficulty priors alongside adversarial debiasing to mitigate the model’s over-reliance on simplistic absolute difficulty shortcuts. Experimental results show that DPAL significantly outperforms both conventional pre-trained baselines and large language models, improving the Macro of the best baseline (XLNet) from 74.19% to 77.81%. Our evaluations also reveal that large language models underperform in zero- and few-shot settings without fine-tuning or Chain-of-Thought prompting. Overall, this work promotes the practical deployment of LCP and lays a crucial technical foundation for dynamic text adaptation within leveled reading.

先秦汉语在汉语史研究上具有重要地位,然而以往的研究始终没有形成结构化的先秦词汇资源,难以满足古汉语信息处理和跨语言对比的研究需要。国际上以英文词网(WordNet)的义类架构为基础,已经建立了数十种语言的词网,已经成为多语言自然语言处理和跨语言对比的基础资源。本文综述了国内外各种词网的构建情况,特别是古代语言的词网和汉语词网,然后详细介绍了先秦词网的构建和校正过程,构建起了涵盖43591个词语、61227个义项、17975个义类的先秦汉语词网。本文还通过与古梵语词网的跨语言对比,尝试分析这两种古老语言在词汇上的共性和差异,初步验证先秦词网的有效性。