Batch Prompting Suppresses Overthinking Reasoning Under Constraint: How Batch Prompting Suppresses Overthinking in Reasoning Models

• URL: http://arxiv.org/abs/2511.04108v1
• Date: Thu, 06 Nov 2025 06:47:39 GMT
• Title: Batch Prompting Suppresses Overthinking Reasoning Under Constraint: How Batch Prompting Suppresses Overthinking in Reasoning Models
• Authors: Wenmo Qiu, Saurabh Srivastava,
• Abstract summary: We show that it regularizes model behavior during multi-step reasoning for Large Reasoning Models (LRMs)<n>We conduct a comprehensive study across 13 diverse benchmarks and observe that improves accuracy while substantially reducing reasoning token usage.<n>Surprisingly, we also observe emergent collective effects in batched inference: models often generalize patterns from earlier examples to solve harder ones.
• Score: 5.408799241182959
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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