Search-R3：在大型语言模型中统一推理与嵌入
Search-R3: Unifying Reasoning and Embedding in Large Language Models
AI总结：
Search-R3通过统一推理与嵌入生成过程，提升大型语言模型在检索任务中的性能，采用监督学习、强化学习和专用环境机制实现有效嵌入生成。
摘要：
尽管大型语言模型（LLMs）在自然语言理解方面表现出色，但它们在检索任务中被低估了。我们提出了Search-R3，一种新的框架，通过将LLMs适应为生成搜索嵌入作为推理过程的直接输出来解决这一限制。我们的方法利用LLMs的推理链能力，通过逐步推理复杂语义分析来生成更有效的嵌入。我们通过三种互补机制实现这一点：（1）监督学习阶段使模型能够生成高质量的嵌入；（2）强化学习（RL）方法优化嵌入生成与推理；（3）专用RL环境高效处理演变的嵌入表示，而无需在每次训练迭代中重新编码整个语料库。我们在多样化的基准测试中进行了广泛的评估，证明Search-R3在统一推理和嵌入生成过程方面显著优于先前方法。这种集成的后训练方法在需要复杂知识密集型任务的处理中代表了重大进步，这些任务需要复杂的推理和有效的信息检索。项目页面：https://github.com/ytgui/Search-R3
英文摘要：
Despite their remarkable natural language understanding capabilities, Large Language Models (LLMs) have been underutilized for retrieval tasks. We present Search-R3, a novel framework that addresses this limitation by adapting LLMs to generate search embeddings as a direct output of their reasoning process. Our approach exploits LLMs' chain-of-thought capabilities, allowing them to produce more effective embeddings by reasoning step-by-step through complex semantic analyses. We implement this through three complementary mechanisms. (1) a supervised learning stage enables the model's ability to produce quality embeddings, (2) a reinforcement learning (RL) methodology that optimizes embedding generation alongside reasoning, and (3) a specialized RL environment that efficiently handles evolving embedding representations without requiring complete corpus re-encoding at each training iteration. Our extensive evaluations on diverse benchmarks demonstrate that Search-R3 significantly outperforms prior methods by unifying the reasoning and embedding generation processes. This integrated post-training approach represents a substantial advancement in handling complex knowledge-intensive tasks that require both sophisticated reasoning and effective information retrieval. Project page: https://github.com/ytgui/Search-R3
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
