Recent work has explored batch prompting as a strategy to amortize inference cost in large language models (LLMs). In this paper, we show that batching offers an additional, underappreciated benefit: it regularizes model behavior during multi-step reasoning for Large Reasoning Models (LRMs). We conduct a comprehensive study across 13 diverse benchmarks and observe that batching improves accuracy while substantially reducing reasoning token usage, often by 3x-5x. Through detailed behavioral analysis, we find that batching suppresses overthinking, reduces hedging language (e.g., repetitive self-corrections), and encourages more decisive answers. Surprisingly, we also observe emergent collective effects in batched inference: models often generalize patterns from earlier examples to solve harder ones in the same batch. These findings position batching not just as a throughput optimization, but as a powerful inference-time regularizer for more efficient and reliable LLM reasoning.

翻译：近期研究探索了批量提示作为大型语言模型（LLMs）中分摊推理成本的一种策略。本文中，我们表明批处理提供了一个未被充分认识的额外优势：它在大型推理模型（LRMs）的多步推理过程中规范化了模型行为。我们在13个多样化基准测试中进行了全面研究，观察到批处理提高了准确性，同时显著减少了推理令牌使用量，通常降低3至5倍。通过详细的行为分析，我们发现批处理抑制了过度思考，减少了模棱两可的语言表达（例如重复的自我修正），并鼓励更果断的答案。令人惊讶的是，我们还观察到批处理推理中出现了集体涌现效应：模型经常从同一批次中较早的示例中泛化模式来解决更难的示例。这些发现将批处理定位为不仅是一种吞吐量优化手段，更是一种强大的推理时规范化器，可实现更高效可靠的LLM推理。