Recent progress in large language model (LLM) reasoning has focused on domains like mathematics and coding, where abundant high-quality data and objective evaluation metrics are readily available. In contrast, progress in scientific reasoning remains limited in domains such as medicine and materials science due to restricted dataset coverage and the inherent complexity of open-ended scientific questions. To address these challenges, we propose a general framework for sustainable scientific reasoning QA generation, and introduce WildSci, a new dataset of domain-specific science questions automatically synthesized from peer-reviewed literature, spanning 9 scientific disciplines and 26 subdomains. WildSci enables scalable training with well-defined reward signals in a multiple-choice format. We further apply reinforcement learning to finetune models on WildSci and analyze the resulting training dynamics, including domain-specific performance changes, response behaviors, and generalization trends. Experiments on a suite of scientific benchmarks demonstrate the effectiveness of our framework and dataset. We release WildSci to enable scalable and sustainable research in scientific reasoning.

Recently, scaling test-time compute on Large Language Models (LLM) has garnered wide attention. However, there has been limited investigation of how various reasoning prompting strategies perform as scaling. In this paper, we focus on a standard and realistic scaling setting: majority voting. We systematically conduct experiments on 6 LLMs × 8 prompting strategies × 6 benchmarks. Experiment results consistently show that as the sampling time and computational overhead increase, complicated prompting strategies with superior initial performance gradually fall behind simple Chain-of-Thought.We analyze this phenomenon and provide theoretical proofs. Additionally, we propose a probabilistic method to efficiently predict scaling performance and identify the best prompting strategy under large sampling times, eliminating the need for resource-intensive inference processes in practical applications.Furthermore, we introduce two ways derived from our theoretical analysis to significantly improve the scaling performance. We hope that our research can promote to re-examine the role of complicated prompting, unleash the potential of simple prompting strategies, and provide new insights for enhancing test-time scaling performance. Code is available at https://github.com/MraDonkey/rethinking_prompting.