Meta AI 推出了 ReasonIR-8B，这是一个专为推理密集型信息检索设计的检索模型。该模型基于 LLaMA3.1-8B 训练，在 BRIGHT 基准测试中建立了新的性能标准，与轻量级 Qwen2.5 重新排序器结合使用时，实现了 36.9 的标准化折扣累积增益 (nDCG@10)。ReasonIR-8B 采用了一种新颖的数据生成流程 ReasonIR-SYNTHESIZER，该流程构建了合成查询和文档对，以反映现实世界推理任务带来的挑战。 该模型已在 Hugging Face 上开源发布，并附带训练代码和合成数据工具，以促进进一步的研究和可重复性。

🧠 ReasonIR-8B 是一款由 Meta AI 推出的检索模型，专为解决复杂推理任务而设计，旨在改进检索增强生成（RAG）系统的性能。

⚙️ 该模型基于 LLaMA3.1-8B 训练，采用双编码器架构，通过余弦相似度对查询和文档进行独立编码和评分。

💡 训练过程中，ReasonIR-8B 采用了创新的 ReasonIR-SYNTHESIZER 数据生成流程，生成了两种类型的训练实例：变长（VL）查询和硬查询（HQ）。VL 查询是信息量丰富的长查询，HQ 查询则需要逻辑推理，以提高模型处理复杂推理场景的能力。

📈 在 BRIGHT 基准测试中，ReasonIR-8B 表现出色，与 Qwen2.5 重新排序器结合使用时，nDCG@10 达到 36.9，超越了其他大型 LLM 重新排序器，同时计算成本更低。

🚀 该模型在检索增强生成（RAG）任务中也取得了显著改进，例如在 MMLU 上提高了 6.4%，在 GPQA 上提高了 22.6%。

🌍 Meta AI 将该模型、代码库和训练数据生成流程开源，鼓励研究人员进一步探索和改进检索模型，以应对更复杂、多语言和多模态的检索需求。

Addressing the Challenges in Reasoning-Intensive Retrieval

Despite notable progress in retrieval-augmented generation (RAG) systems, retrieving relevant information for complex, multi-step reasoning tasks remains a significant challenge. Most retrievers today are trained on datasets composed of short factual questions, which align well with document-level lexical or semantic overlaps. However, they fall short when faced with longer, abstract, or cross-domain queries that require synthesizing dispersed knowledge. In such cases, retrieval errors can propagate through the pipeline, impairing downstream reasoning by large language models (LLMs). While LLM-based rerankers can improve relevance, their substantial computational cost often renders them impractical in real-world deployments.

Meta AI Introduces ReasonIR-8B, a Retriever Built for Reasoning

Meta AI has released ReasonIR-8B, a retriever model designed explicitly for reasoning-intensive information retrieval. Trained from LLaMA3.1-8B, the model establishes new performance standards on the BRIGHT benchmark, achieving a normalized Discounted Cumulative Gain (nDCG@10) of 36.9 when used with a lightweight Qwen2.5 reranker. Notably, it surpasses leading reranking models such as Rank1-32B while offering 200× lower inference-time compute, making it significantly more practical for scaled RAG applications.

ReasonIR-8B is trained using a novel data generation pipeline, ReasonIR-SYNTHESIZER, which constructs synthetic queries and document pairs that mirror the challenges posed by real-world reasoning tasks. The model is released open-source on Hugging Face, along with training code and synthetic data tools, enabling further research and reproducibility.

Model Architecture, Training Pipeline, and Key Innovations

ReasonIR-8B employs a bi-encoder architecture, where queries and documents are encoded independently into embeddings and scored via cosine similarity. The model’s training relies heavily on synthetically generated data tailored to reasoning scenarios. The ReasonIR-SYNTHESIZER pipeline produces two primary types of training instances:

Varied-Length (VL) Queries: These are long, information-rich queries (up to 2000 tokens), paired with corresponding documents, encouraging the retriever to handle extended contexts effectively.Hard Queries (HQ): Derived from curated documents with high educational value, these queries are designed to require logical inference. Multi-turn prompts are used to construct hard negatives—documents that appear superficially relevant but do not contain the necessary reasoning pathways.

This approach contrasts with conventional negative sampling methods, which often rely on lexical overlap and are less effective for abstract or multi-hop questions.

Additionally, the model’s attention mask is modified from LLaMA’s causal configuration to a bi-directional one, allowing the encoder to consider the full query context symmetrically, which is beneficial for non-sequential semantic alignment.

Empirical Results on IR and RAG Benchmarks

ReasonIR-8B achieves strong performance across several benchmarks:

BRIGHT Benchmark (Reasoning-Intensive Retrieval):
24.4 nDCG@10 on original queries29.9 with GPT-4 rewritten queries36.9 with Qwen2.5 reranking, outperforming larger LLM rerankers at a fraction of the cost
Retrieval-Augmented Generation (RAG) Tasks:
+6.4% improvement on MMLU over a closed-book baseline+22.6% improvement on GPQA

These gains are consistent across both standard and rewritten queries, with further improvements observed when combining REASONIR-8B with a sparse retriever like BM25 or a lightweight reranker.

Importantly, the model continues to improve as query lengths scale, unlike other retrievers whose performance plateaus or declines. This suggests that ReasonIR-8B can better exploit information-rich queries, making it particularly well-suited for test-time techniques such as query rewriting.

Conclusion

ReasonIR-8B addresses a key bottleneck in reasoning-focused information retrieval by introducing a retriever optimized not only for relevance but also for computational efficiency. Its design—rooted in synthetic training tailored for reasoning, coupled with architectural and data-centric improvements—enables consistent gains in both retrieval and RAG tasks.

By releasing the model, codebase, and training data generation pipeline as open-source tools, Meta AI encourages the research community to extend this work toward more robust, multilingual, and multimodal retrievers. For applications requiring cost-effective and high-quality retrieval under reasoning constraints, ReasonIR-8B represents a compelling and practical solution.

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