用24个标注样例实现高质量结构化推理，通过精炼数据提升大模型逻辑能力。
LLMSR@XLLM25: Less is More: Enhancing Structured Multi-Agent Reasoning via Quality-Guided Distillation
- 构建多智能体框架，结合反向提示与检索增强生成高质量推理链
- 仅用24个标注样本，在三项任务上均显著提升结构化推理准确率
- 适合低资源场景下需要可解释推理的AI系统研发者参考
LLMSR@XLLM25提出一项低资源结构化推理任务，要求大模型在极少标注数据下生成可解释的分步推理过程。本文提出的「Less is More」方法是该挑战第三名方案，仅使用24个标注样本，通过多智能体框架实现逆向提示诱导、基于GPT-4o的检索增强推理合成，以及双阶段奖励引导过滤，完成对三个子任务（问题解析、思维链解析、步骤级验证）的高质量监督知识蒸馏。所有模块均基于Meta-Llama-3-8B-Instruct，在统一的LoRA+微调设置下训练。通过结合结构验证与奖励过滤机制，无论在少样本还是零样本提示下，该流程均持续提升结构化推理质量。结果表明，在低资源条件下，可控的数据蒸馏对增强结构化推理具有重要价值。代码已开源：https://github.com/JhCircle/Less-is-More。
原文摘要 · Abstract (English)
The LLMSR@XLLM25 formulates a low-resource structural reasoning task that challenges LLMs to generate interpretable, step-by-step rationales with minimal labeled data. We present Less is More, the third-place winning approach in the LLMSR@XLLM25, which focuses on structured reasoning from only 24 labeled examples. Our approach leverages a multi-agent framework with reverse-prompt induction, retrieval-augmented reasoning synthesis via GPT-4o, and dual-stage reward-guided filtering to distill high-quality supervision across three subtasks: question parsing, CoT parsing, and step-level verification. All modules are fine-tuned from Meta-Llama-3-8B-Instruct under a unified LoRA+ setup. By combining structure validation with reward filtering across few-shot and zero-shot prompts, our pipeline consistently improves structure reasoning quality. These results underscore the value of controllable data distillation in enhancing structured inference under low-resource constraints. Our code is available at https://github.com/JhCircle/Less-is-More.
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