Linxuan Du, Guangquan Xue, Xiaobo Liang, Qipeng Huang, Yuyang Ding, Xinyu Shi, Zhang Yijun China Mobile Communications Company Limited Research Institute, Ji Qi 等 11 位作者
Despite the potential of multi-turn self-reflection to improve LLM reasoning, its effectiveness in practice is severely constrained by a failure mode we term the Echo Trap.Specifically, this phenomenon gives rise to two coupled problems: (1) the model becomes limited by its inherent capabilities and tends to repeat earlier reflections to preserve reward signals; (2) once such “copy” behavior is reinforced, the model ceases to try new strategies, leading to exploration collapse.We attribute this issue to imprecise credit assignment during training, as standard GRPO assigns rewards at the trajectory level, making it difficult to distinguish which reflection steps contribute to improved outcomes.To address this limitation, we propose a tree-structured extension of GRPO for multi-turn self-reflection, which enables more accurate advantage estimation.Through extensive experiments, we analyze the Echo Trap and demonstrate that our method effectively mitigates behavior collapse and improves performance across multiple benchmarks.
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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
Yuzhen Shi Alibaba Group, Huanghai Liu Tsinghua University, Tsinghua University, Yiran HU, Song Gaojie, Xu Xinran, Yubo Ma, Tianyi Tang Alibaba Group, Li Zhang 等 30 位作者
As large language models (LLMs) are increasingly applied to legal domain-specific tasks, evaluating their ability to perform legal work in real-world settings has become essential. However, existing legal benchmarks rely on simplified and highly standardized tasks, failing to capture the ambiguity, complexity, and reasoning demands of real legal practice. Moreover, prior evaluations often adopt coarse, single-dimensional metrics and do not explicitly assess fine-grained legal reasoning. To address these limitations, we introduce PLawBench, a Practical Law Benchmark designed to evaluate LLMs in realistic legal practice scenarios. Grounded in real-world legal workflows, PLawBench models the core processes of legal practitioners through three task categories: public legal consultation, practical case analysis, and legal document generation. These tasks assess a model’s ability to identify legal issues and key facts, perform structured legal reasoning, and generate legally coherent documents. PLawBench comprises 850 questions across 13 practical legal scenarios, with each question accompanied by expert-designed evaluation rubrics, resulting in approximately 12,500 rubric items for fine-grained assessment. Using an LLM-based evaluator aligned with human expert judgments, we evaluate 10 state-of-the-art LLMs. Experimental results show that none achieves strong performance on PLawBench, revealing substantial limitations in the fine-grained legal reasoning capabilities of current LLMs and highlighting important directions for future evaluation and development of legal LLMs. Data is available at: https://anonymous.4open.science/r/PLawbench-B524/.
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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