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The paper introduces Diverse Parallel Thinking (DPT), a reinforcement learning approach that encourages path diversity in parallel reasoning for large language models. DPT penalizes prefix similarity among parallel paths, fostering distinct reasoning strategies and improving exploration of the reasoning space. Experiments on multiple benchmarks show that DPT outperforms prior parallel thinking methods, achieving state‑of‑the‑art results.
arXiv:2610.35609v1 Announce Type: cross Abstract: Modeling the latent, multi-step reasoning processes of learners is a core challenge for adaptive tutoring systems, which often rely on simple right/w...
arXiv:2504.02323v5 Announce Type: replace Abstract: Large language models (LLMs) have created new opportunities to assist teachers and support student learning. While researchers have explored variou...
arXiv:2610.15204v1 Announce Type: new Abstract: Large language models are increasingly used in computer science education. However, the teaching abilities of even the most powerful LLMs are limited:...
EduDial is a large-scale multi-turn teacher‑student dialogue corpus covering 345 core knowledge points and 34,250 dialogue sessions, designed around Bloom’s taxonomy and ten questioning strategies such as situational, ZPD, and metacognitive questioning. The dataset includes differentiated teaching strategies for students at varying cognitive levels to provide targeted guidance. Using EduDial, the authors trained EduDial‑LLM 32B and introduced an 11‑dimensional evaluation framework that measures teaching quality and content quality, showing that most mainstream LLMs struggle with student‑centered teaching while EduDial‑LLM outperforms all baselines across all metrics.
arXiv:2509.05346v3 Announce Type: replace Abstract: While large language models (LLMs) are increasingly being adopted to support personalized learning, there remains limited understanding of how thei...