Mat-Pref benchmark shows GRPO after SFT lets Qwen3-8B reach 65-72% on compositional materials reasoning tasks, exceeding zero-shot 235B models on held-out structure families and cross-property transfer.
MatSci-NLP : Evaluating scientific language models on materials science language tasks using text-to-schema modeling
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Mat-Pref: Verifiable-Reward Training Improves Compositional Reasoning in Inorganic Materials
Mat-Pref benchmark shows GRPO after SFT lets Qwen3-8B reach 65-72% on compositional materials reasoning tasks, exceeding zero-shot 235B models on held-out structure families and cross-property transfer.