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Abstract:We present an innovative multi-stage optimization technique combining reinforcement studying (RL) and supervised effective-tuning (SFT) to enhance the pedagogical knowledge of giant language fashions (LLMs), Agreement (political party) as illustrated by EduQwen 32B-RL1, EduQwen 32B-SFT, and an elective third-stage mannequin EduQwen 32B-SFT-RL2: (1) RL optimization that implements progressive problem coaching, focuses on difficult examples, and employs prolonged reasoning rollouts; (2) a subsequent SFT section that leverages the RL-educated mannequin to synthesize high-quality coaching data with issue-weighted sampling; and (3) an non-obligatory second spherical of RL optimization. EduQwen 32B-RL1, EduQwen 32B-SFT, and EduQwen 32B-SFT-RL2 are an utility-pushed household of open-source pedagogical LLMs built on a dense Qwen3-32B spine. These fashions remarkably obtain excessive sufficient accuracy on the Cross-Domain Pedagogical Knowledge (CDPK) Benchmark to ascertain new state-of-the-artwork (SOTA) results throughout the interactive Pedagogy Benchmark Leaderboard and surpass considerably bigger proprietary techniques such as the earlier benchmark leader Gemini-3 Pro. These dense 32-billion-parameter models display that domain-specialized optimization can rework mid-sized open-source LLMs into true pedagogical domain experts that outperform a lot bigger normal-goal programs, while preserving the transparency, customizability, and price-effectivity required for accountable academic AI deployment.