Pay for Hints, Not Answers: LLM Shepherding for Cost-Efficient Inference

• URL: http://arxiv.org/abs/2601.22132v1
• Date: Thu, 29 Jan 2026 18:52:54 GMT
• Title: Pay for Hints, Not Answers: LLM Shepherding for Cost-Efficient Inference
• Authors: Ziming Dong, Hardik Sharma, Evan O'Toole, Jaya Prakash Champati, Kui Wu,
• Abstract summary: Small Language Models (SLMs) offer dramatic cost savings yet lag substantially in accuracy.<n>We introduce LLM Shepherding, a framework that requests only a short prefix (a hint) from the LLM and provides it to SLM.<n>Shepherding generalizes both routing and cascading, and it achieves lower cost under oracle decision-making.
• Score: 7.865726406769634
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) deliver state-of-the-art performance on complex reasoning tasks, but their inference costs limit deployment at scale. Small Language Models (SLMs) offer dramatic cost savings yet lag substantially in accuracy. Existing approaches - routing and cascading - treat the LLM as an all-or-nothing resource: either the query bypasses the LLM entirely, or the LLM generates a complete response at full cost. We introduce LLM Shepherding, a framework that requests only a short prefix (a hint) from the LLM and provides it to SLM. This simple mechanism is surprisingly effective for math and coding tasks: even hints comprising 10-30% of the full LLM response improve SLM accuracy significantly. Shepherding generalizes both routing and cascading, and it achieves lower cost under oracle decision-making. We develop a two-stage predictor that jointly determines whether a hint is needed and how many tokens to request. On the widely-used mathematical reasoning (GSM8K, CNK12) and code generation (HumanEval, MBPP) benchmarks, Shepherding reduces costs by 42-94% relative to LLM-only inference. Compared to state-of-the-art routing and cascading baselines, shepherding delivers up to 2.8x cost reduction while matching accuracy. To our knowledge, this is the first work to exploit token-level budget control for SLM-LLM collaboration.

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