Large Language Models (LLMs) have demonstrated remarkable general capabilities, yet they falter in domains requiring deep strategic reasoning.A primary obstacle is the need to navigate a game tree that grows exponentially with search depth, a task for which their generative nature is ill-suited.To address this, we introduce Generative Gamer (GenGamer), a framework that trains LLMs to reason like an expert player.Instead of attempting an exhaustive search, GenGamer learns to generate a compact, pruned reasoning trajectory termed as a Dynamic Deduction.This is achieved by integrating three key strategies: action pruning based on policy confidence, state pruning via value estimation, and branch pruning inspired by alpha-beta principles.Furthermore, to train the model effectively, we propose the Deduction Tree Reward (DTR), a process-oriented mechanism that provides step-by-step feedback on the quality of the reasoning process, rather than relying solely on the final game outcome.Experiments on complex games such as Tic-Tac-Toe and Leduc Poker demonstrate that GenGamer significantly enhances the strategic capabilities of LLMs, enabling them to achieve performance that surpasses current state-of-theart language models.

This paper introduces the Chinese Essay Discourse Coherence Corpus (CEDCC), a multi-task dataset for assessing discourse coherence.Existing research tends to focus on isolated dimensions of discourse coherence, a gap which the CEDCC addresses by integrating coherence grading, topical continuity, and discourse relations.This approach, alongside detailed annotations, captures the subtleties of real-world texts and stimulates progress in Chinese discourse coherence analysis.Our contributions include the development of the CEDCC, the establishment of baselines for further research, and the demonstration of the impact of coherence on discourse relation recognition and automated essay scoring.The dataset and related codes is available at https: //github.com/cubenlp/CEDCC_corpus.

Document-Level Relation Extraction(DLRE) is a more challenging task than sentence-level relation extraction because of the characteristics such as more extended context, more interactions between entities, and the need for common sense to help the relation inference. In this paper, we propose an effective model to address the problems of complex entity interactions and the lack of commonsense knowledge. Specifically, we propose a Transformer-based entity interaction module instead of graph neural networks to model the correlation across entities, thus avoiding the information loss problem triggered by predefined edge-building rules. In addition, the initial word vector from the word embedding layer of a pre-trained language model is injected into entity representation to boost the performance in the extraction of relational facts that need commonsense knowledge. Experiments show that our model obtains competitive performance, especially compared with graph-based methods, which is faster and more effective. The source code, trained checkpoint files, and the commit results will be released to the public.